Made Logger global
This commit is contained in:
parent
1f7c6f10ab
commit
c6172f309d
21 changed files with 513 additions and 527 deletions
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@ -14,13 +14,8 @@ This is a dictionary that is shared around the different components. Contains hy
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### Environment
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This component needs to support the standard openai functions reset and step.
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### Logger
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For Tensorboard to work, you need to define a logger that will (optionally) later go into the network, runner, and agent/trainer.
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Due to issues with multiprocessing, the Logger is a shared dictionary of lists that get appended to and the LogWriter writes on the main thread.
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### Network
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A network takes a PyTorch nn.Module, PyTorch optimizer, configuration, and the optional logger.
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A network takes a PyTorch nn.Module, PyTorch optimizer, and configuration.
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### Target Network
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Takes in a network and provides methods to sync a copy of the original network.
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@ -8,6 +8,7 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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from rltorch.log import Logger
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#
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## Networks
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@ -68,65 +69,55 @@ config['disable_cuda'] = False
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#
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## Training Loop
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#
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def train(runner, agent, config, logger = None, logwriter = None):
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def train(runner, agent, config, logwriter=None):
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finished = False
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while not finished:
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runner.run()
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agent.learn()
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if logwriter is not None:
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agent.value_net.log_named_parameters()
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agent.policy_net.log_named_parameters()
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logwriter.write(logger)
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agent.value_net.log_named_parameters()
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agent.policy_net.log_named_parameters()
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logwriter.write(Logger)
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finished = runner.episode_num > config['total_training_episodes']
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if __name__ == "__main__":
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end = " ")
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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end=" ")
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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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# Logging
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logger = rltorch.log.Logger()
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Setting up the networks
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device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
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policy_net = rn.Network(Policy(state_size, action_size),
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torch.optim.Adam, config, device = device, name = "Policy")
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value_net = rn.Network(Value(state_size),
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torch.optim.Adam, config, device = device, name = "DQN")
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# Memory stores experiences for later training
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memory = M.EpisodeMemory()
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# Actor takes a net and uses it to produce actions from given states
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actor = StochasticSelector(policy_net, action_size, memory, device = device)
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# Agent is what performs the training
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agent = rltorch.agents.A2CSingleAgent(policy_net, value_net, memory, config, logger = logger)
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# Runner performs one episode in the environment
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runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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# Logging
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Setting up the networks
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device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
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policy_net = rn.Network(Policy(state_size, action_size),
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torch.optim.Adam, config, device=device, name="Policy")
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value_net = rn.Network(Value(state_size),
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torch.optim.Adam, config, device=device, name="DQN")
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# Memory stores experiences for later training
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memory = M.EpisodeMemory()
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# Actor takes a net and uses it to produce actions from given states
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actor = StochasticSelector(policy_net, action_size, memory, device = device)
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# Agent is what performs the training
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agent = rltorch.agents.A2CSingleAgent(policy_net, value_net, memory, config)
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# Runner performs one episode in the environment
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runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name="Training", memory=memory, logwriter=logwriter)
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print("Training...")
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train(runner, agent, config, logger = logger, logwriter = logwriter)
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print("Training...")
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train(runner, agent, config, logwriter=logwriter)
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# For profiling...
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# import cProfile
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# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
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# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
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# For profiling...
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# import cProfile
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# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
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# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
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print("Training Finished.")
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print("Evaluating...")
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rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
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print("Evaulations Done.")
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logwriter.close() # We don't need to write anything out to disk anymore
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print("Training Finished.")
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print("Evaluating...")
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rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], name="Evaluation")
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print("Evaulations Done.")
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logwriter.close() # We don't need to write anything out to disk anymore
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@ -9,29 +9,28 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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from rltorch.log import Logger
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#
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## Networks
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#
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class Policy(nn.Module):
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def __init__(self, state_size, action_size):
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super(Policy, self).__init__()
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self.state_size = state_size
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self.action_size = action_size
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def __init__(self, state_size, action_size):
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super(Policy, self).__init__()
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self.state_size = state_size
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self.action_size = action_size
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self.fc1 = nn.Linear(state_size, 125)
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self.fc_norm = nn.LayerNorm(125)
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self.fc1 = nn.Linear(state_size, 125)
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self.fc_norm = nn.LayerNorm(125)
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self.fc2 = nn.Linear(125, 125)
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self.fc2_norm = nn.LayerNorm(125)
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self.fc2 = nn.Linear(125, 125)
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self.fc2_norm = nn.LayerNorm(125)
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self.action_prob = nn.Linear(125, action_size)
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self.action_prob = nn.Linear(125, action_size)
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = F.softmax(self.action_prob(x), dim = 1)
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return x
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = F.softmax(self.action_prob(x), dim = 1)
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return x
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#
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## Configuration
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@ -50,75 +49,67 @@ config['disable_cuda'] = False
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#
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## Training Loop
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#
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def train(runner, net, config, logger = None, logwriter = None):
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finished = False
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while not finished:
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runner.run()
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net.calc_gradients()
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net.step()
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if logwriter is not None:
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net.log_named_parameters()
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logwriter.write(logger)
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finished = runner.episode_num > config['total_training_episodes']
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def train(runner, net, config, logwriter=None):
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finished = False
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while not finished:
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runner.run()
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net.calc_gradients()
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net.step()
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if logwriter is not None:
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net.log_named_parameters()
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logwriter.write(Logger)
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finished = runner.episode_num > config['total_training_episodes']
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#
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## Loss function
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#
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def fitness(model):
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env = gym.make("Acrobot-v1")
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state = torch.from_numpy(env.reset()).float().unsqueeze(0)
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total_reward = 0
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done = False
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while not done:
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action_probabilities = model(state)
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distribution = Categorical(action_probabilities)
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action = distribution.sample().item()
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next_state, reward, done, _ = env.step(action)
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total_reward += reward
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state = torch.from_numpy(next_state).float().unsqueeze(0)
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return -total_reward
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env = gym.make("Acrobot-v1")
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state = torch.from_numpy(env.reset()).float().unsqueeze(0)
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total_reward = 0
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done = False
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while not done:
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action_probabilities = model(state)
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distribution = Categorical(action_probabilities)
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action = distribution.sample().item()
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next_state, reward, done, _ = env.step(action)
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total_reward += reward
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state = torch.from_numpy(next_state).float().unsqueeze(0)
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return -total_reward
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if __name__ == "__main__":
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# Hide internal gym warnings
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gym.logger.set_level(40)
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# Hide internal gym warnings
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gym.logger.set_level(40)
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end = " ")
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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end=" ")
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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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# Logging
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logger = rltorch.log.Logger()
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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# Setting up the networks
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device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
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net = rn.ESNetwork(Policy(state_size, action_size),
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torch.optim.Adam, 100, fitness, config, device = device, name = "ES", logger = logger)
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# Logging
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Setting up the networks
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device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
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net = rn.ESNetwork(Policy(state_size, action_size),
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torch.optim.Adam, 100, fitness, config, device=device, name="ES")
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# Actor takes a net and uses it to produce actions from given states
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actor = StochasticSelector(net, action_size, device=device)
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# Runner performs an episode of the environment
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runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name="Training", logwriter=logwriter)
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print("Training...")
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train(runner, net, config, logwriter=logwriter)
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# For profiling...
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# import cProfile
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# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
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# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
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print("Training Finished.")
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print("Evaluating...")
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rltorch.env.simulateEnvEps(env, actor, config, total_episodes=config['total_evaluation_episodes'], name="Evaluation")
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print("Evaulations Done.")
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# Actor takes a net and uses it to produce actions from given states
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actor = StochasticSelector(net, action_size, device = device)
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# Runner performs an episode of the environment
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runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", logwriter = logwriter)
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print("Training...")
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train(runner, net, config, logger = logger, logwriter = logwriter)
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# For profiling...
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# import cProfile
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# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
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# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
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print("Training Finished.")
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print("Evaluating...")
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rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
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print("Evaulations Done.")
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logwriter.close() # We don't need to write anything out to disk anymore
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logwriter.close() # We don't need to write anything out to disk anymore
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@ -8,48 +8,49 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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from rltorch.log import Logger
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#
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## Networks
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#
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class Value(nn.Module):
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def __init__(self, state_size):
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super(Value, self).__init__()
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self.state_size = state_size
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def __init__(self, state_size):
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super(Value, self).__init__()
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self.state_size = state_size
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self.fc1 = rn.NoisyLinear(state_size, 64)
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self.fc_norm = nn.LayerNorm(64)
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self.fc1 = rn.NoisyLinear(state_size, 64)
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self.fc_norm = nn.LayerNorm(64)
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self.fc2 = rn.NoisyLinear(64, 64)
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self.fc2_norm = nn.LayerNorm(64)
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self.fc2 = rn.NoisyLinear(64, 64)
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self.fc2_norm = nn.LayerNorm(64)
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self.fc3 = rn.NoisyLinear(64, 1)
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self.fc3 = rn.NoisyLinear(64, 1)
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = self.fc3(x)
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return x
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = self.fc3(x)
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return x
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class Policy(nn.Module):
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def __init__(self, state_size, action_size):
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super(Policy, self).__init__()
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self.state_size = state_size
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self.action_size = action_size
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def __init__(self, state_size, action_size):
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super(Policy, self).__init__()
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self.state_size = state_size
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self.action_size = action_size
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self.fc1 = rn.NoisyLinear(state_size, 64)
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self.fc_norm = nn.LayerNorm(64)
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self.fc1 = rn.NoisyLinear(state_size, 64)
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self.fc_norm = nn.LayerNorm(64)
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self.fc2 = rn.NoisyLinear(64, 64)
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self.fc2_norm = nn.LayerNorm(64)
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self.fc2 = rn.NoisyLinear(64, 64)
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self.fc2_norm = nn.LayerNorm(64)
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self.fc3 = rn.NoisyLinear(64, action_size)
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self.fc3 = rn.NoisyLinear(64, action_size)
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = F.softmax(self.fc3(x), dim = 1)
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return x
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = F.softmax(self.fc3(x), dim = 1)
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return x
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#
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## Configuration
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@ -68,64 +69,63 @@ config['disable_cuda'] = False
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#
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## Training Loop
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#
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def train(runner, agent, config, logger = None, logwriter = None):
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def train(runner, agent, config, logwriter = None):
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finished = False
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while not finished:
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runner.run()
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agent.learn()
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if logwriter is not None:
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agent.value_net.log_named_parameters()
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agent.policy_net.log_named_parameters()
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logwriter.write(logger)
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agent.value_net.log_named_parameters()
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agent.policy_net.log_named_parameters()
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logwriter.write(Logger)
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finished = runner.episode_num > config['total_training_episodes']
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if __name__ == "__main__":
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end = " ")
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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end=" ")
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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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# Logging
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logger = rltorch.log.Logger()
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Logging
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
policy_net = rn.Network(Policy(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "Policy")
|
||||
value_net = rn.Network(Value(state_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN")
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
policy_net = rn.Network(Policy(state_size, action_size),
|
||||
torch.optim.Adam, config, device=device, name="Policy")
|
||||
value_net = rn.Network(Value(state_size),
|
||||
torch.optim.Adam, config, device=device, name="DQN")
|
||||
|
||||
|
||||
# Memory stores experiences for later training
|
||||
memory = M.EpisodeMemory()
|
||||
# Memory stores experiences for later training
|
||||
memory = M.EpisodeMemory()
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = StochasticSelector(policy_net, action_size, memory, device = device)
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = StochasticSelector(policy_net, action_size, memory, device=device)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.PPOAgent(policy_net, value_net, memory, config, logger = logger)
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.PPOAgent(policy_net, value_net, memory, config)
|
||||
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name="Training", memory=memory, logwriter=logwriter)
|
||||
|
||||
print("Training...")
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
print("Training...")
|
||||
train(runner, agent, config, logwriter=logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
print("Training Finished.")
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes=config['total_evaluation_episodes'], name="Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
|
|
|
|||
|
|
@ -7,61 +7,62 @@ import rltorch.network as rn
|
|||
import rltorch.memory as M
|
||||
import rltorch.env as E
|
||||
from rltorch.action_selector import StochasticSelector
|
||||
from tensorboardX import SummaryWriter
|
||||
# from tensorboardX import SummaryWriter
|
||||
from copy import deepcopy
|
||||
from rltorch.log import Logger
|
||||
|
||||
#
|
||||
## Networks
|
||||
#
|
||||
class Value(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.fc1 = rn.NoisyLinear(state_size, 255)
|
||||
self.fc_norm = nn.LayerNorm(255)
|
||||
self.fc1 = rn.NoisyLinear(state_size, 255)
|
||||
self.fc_norm = nn.LayerNorm(255)
|
||||
|
||||
self.value_fc = rn.NoisyLinear(255, 255)
|
||||
self.value_fc_norm = nn.LayerNorm(255)
|
||||
self.value = rn.NoisyLinear(255, 1)
|
||||
|
||||
self.advantage_fc = rn.NoisyLinear(255, 255)
|
||||
self.advantage_fc_norm = nn.LayerNorm(255)
|
||||
self.advantage = rn.NoisyLinear(255, action_size)
|
||||
self.value_fc = rn.NoisyLinear(255, 255)
|
||||
self.value_fc_norm = nn.LayerNorm(255)
|
||||
self.value = rn.NoisyLinear(255, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
self.advantage_fc = rn.NoisyLinear(255, 255)
|
||||
self.advantage_fc_norm = nn.LayerNorm(255)
|
||||
self.advantage = rn.NoisyLinear(255, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
|
||||
x = state_value + advantage - advantage.mean()
|
||||
return x
|
||||
x = state_value + advantage - advantage.mean()
|
||||
return x
|
||||
|
||||
|
||||
class Policy(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Policy, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Policy, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.fc1 = nn.Linear(state_size, 125)
|
||||
self.fc_norm = nn.LayerNorm(125)
|
||||
self.fc1 = nn.Linear(state_size, 125)
|
||||
self.fc_norm = nn.LayerNorm(125)
|
||||
|
||||
self.fc2 = nn.Linear(125, 125)
|
||||
self.fc2_norm = nn.LayerNorm(125)
|
||||
self.fc2 = nn.Linear(125, 125)
|
||||
self.fc2_norm = nn.LayerNorm(125)
|
||||
|
||||
self.action_prob = nn.Linear(125, action_size)
|
||||
self.action_prob = nn.Linear(125, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
x = F.relu(self.fc2_norm(self.fc2(x)))
|
||||
x = F.softmax(self.action_prob(x), dim = 1)
|
||||
return x
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
x = F.relu(self.fc2_norm(self.fc2(x)))
|
||||
x = F.softmax(self.action_prob(x), dim = 1)
|
||||
return x
|
||||
|
||||
#
|
||||
## Configuration
|
||||
|
|
@ -94,70 +95,70 @@ config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialSc
|
|||
#
|
||||
## Training Loop
|
||||
#
|
||||
def train(runner, agent, config, logger = None, logwriter = None):
|
||||
def train(runner, agent, config, logwriter=None):
|
||||
finished = False
|
||||
last_episode_num = 1
|
||||
while not finished:
|
||||
runner.run(config['replay_skip'] + 1)
|
||||
agent.learn()
|
||||
if logwriter is not None:
|
||||
if last_episode_num < runner.episode_num:
|
||||
last_episode_num = runner.episode_num
|
||||
agent.value_net.log_named_parameters()
|
||||
agent.policy_net.log_named_parameters()
|
||||
logwriter.write(logger)
|
||||
if last_episode_num < runner.episode_num:
|
||||
last_episode_num = runner.episode_num
|
||||
agent.value_net.log_named_parameters()
|
||||
agent.policy_net.log_named_parameters()
|
||||
logwriter.write(Logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
# Logging
|
||||
logger = rltorch.log.Logger()
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
# Logging
|
||||
logwriter = None
|
||||
# logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
config2 = deepcopy(config)
|
||||
config2['learning_rate'] = 0.01
|
||||
policy_net = rn.ESNetwork(Policy(state_size, action_size),
|
||||
torch.optim.Adam, 500, None, config2, sigma = 0.1, device = device, name = "ES", logger = logger)
|
||||
value_net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN", logger = logger)
|
||||
target_net = rn.TargetNetwork(value_net, device = device)
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
config2 = deepcopy(config)
|
||||
config2['learning_rate'] = 0.01
|
||||
policy_net = rn.ESNetwork(Policy(state_size, action_size),
|
||||
torch.optim.Adam, 500, None, config2, sigma=0.1, device=device, name="ES")
|
||||
value_net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device=device, name="DQN")
|
||||
target_net = rn.TargetNetwork(value_net, device=device)
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = StochasticSelector(policy_net, action_size, device = device)
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = StochasticSelector(policy_net, action_size, device=device)
|
||||
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity=config['memory_size'], alpha=config['prioritized_replay_sampling_priority'])
|
||||
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name="Training", memory=memory, logwriter=logwriter)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.QEPAgent(policy_net, value_net, memory, config, target_value_net = target_net, logger = logger)
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.QEPAgent(policy_net, value_net, memory, config, target_value_net=target_net)
|
||||
|
||||
print("Training...")
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
print("Training...")
|
||||
train(runner, agent, config, logwriter=logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
print("Training Finished.")
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], name="Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
# logwriter.close() # We don't need to write anything out to disk anymore
|
||||
|
|
|
|||
|
|
@ -7,30 +7,30 @@ import rltorch.network as rn
|
|||
import rltorch.memory as M
|
||||
import rltorch.env as E
|
||||
from rltorch.action_selector import StochasticSelector
|
||||
from tensorboardX import SummaryWriter
|
||||
from rltorch.log import Logger
|
||||
|
||||
#
|
||||
## Networks
|
||||
#
|
||||
class Policy(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Policy, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Policy, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.fc1 = rn.NoisyLinear(state_size, 64)
|
||||
self.fc_norm = nn.LayerNorm(64)
|
||||
self.fc1 = rn.NoisyLinear(state_size, 64)
|
||||
self.fc_norm = nn.LayerNorm(64)
|
||||
|
||||
self.fc2 = rn.NoisyLinear(64, 64)
|
||||
self.fc2_norm = nn.LayerNorm(64)
|
||||
self.fc2 = rn.NoisyLinear(64, 64)
|
||||
self.fc2_norm = nn.LayerNorm(64)
|
||||
|
||||
self.fc3 = rn.NoisyLinear(64, action_size)
|
||||
self.fc3 = rn.NoisyLinear(64, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
x = F.relu(self.fc2_norm(self.fc2(x)))
|
||||
x = F.softmax(self.fc3(x), dim = 1)
|
||||
return x
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
x = F.relu(self.fc2_norm(self.fc2(x)))
|
||||
x = F.softmax(self.fc3(x), dim=1)
|
||||
return x
|
||||
|
||||
#
|
||||
## Configuration
|
||||
|
|
@ -49,65 +49,65 @@ config['disable_cuda'] = False
|
|||
#
|
||||
## Training Loop
|
||||
#
|
||||
def train(runner, agent, config, logger = None, logwriter = None):
|
||||
finished = False
|
||||
while not finished:
|
||||
runner.run()
|
||||
agent.learn()
|
||||
# When the episode number changes, log network paramters
|
||||
if logwriter is not None:
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
def train(runner, agent, config, logwriter=None):
|
||||
finished = False
|
||||
while not finished:
|
||||
runner.run()
|
||||
agent.learn()
|
||||
# When the episode number changes, log network paramters
|
||||
if logwriter is not None:
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(Logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
|
||||
torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
|
||||
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end=" ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
# Logging
|
||||
logger = rltorch.log.Logger()
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
# Logging
|
||||
logwriter = None
|
||||
# logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Policy(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN")
|
||||
target_net = rn.TargetNetwork(net, device = device)
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Policy(state_size, action_size),
|
||||
torch.optim.Adam, config, device=device, name="DQN")
|
||||
target_net = rn.TargetNetwork(net, device=device)
|
||||
|
||||
# Memory stores experiences for later training
|
||||
memory = M.EpisodeMemory()
|
||||
# Memory stores experiences for later training
|
||||
memory = M.EpisodeMemory()
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = StochasticSelector(net, action_size, memory, device = device)
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = StochasticSelector(net, action_size, memory, device=device)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net = target_net, logger = logger)
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net=target_net)
|
||||
|
||||
# Runner performs one episode in the environment
|
||||
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
# Runner performs one episode in the environment
|
||||
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name="Training", memory=memory, logwriter=logwriter)
|
||||
|
||||
print("Training...")
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
print("Training...")
|
||||
train(runner, agent, config, logwriter=logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
print("Training Finished.")
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes=config['total_evaluation_episodes'], name="Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
# logwriter.close() # We don't need to write anything out to disk anymore
|
||||
|
|
|
|||
|
|
@ -7,39 +7,39 @@ import rltorch.network as rn
|
|||
import rltorch.memory as M
|
||||
import rltorch.env as E
|
||||
from rltorch.action_selector import ArgMaxSelector
|
||||
from tensorboardX import SummaryWriter
|
||||
from rltorch.log import Logger
|
||||
|
||||
#
|
||||
## Networks
|
||||
#
|
||||
class Value(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.fc1 = rn.NoisyLinear(state_size, 255)
|
||||
self.fc_norm = nn.LayerNorm(255)
|
||||
self.fc1 = rn.NoisyLinear(state_size, 255)
|
||||
self.fc_norm = nn.LayerNorm(255)
|
||||
|
||||
self.value_fc = rn.NoisyLinear(255, 255)
|
||||
self.value_fc_norm = nn.LayerNorm(255)
|
||||
self.value = rn.NoisyLinear(255, 1)
|
||||
self.value_fc = rn.NoisyLinear(255, 255)
|
||||
self.value_fc_norm = nn.LayerNorm(255)
|
||||
self.value = rn.NoisyLinear(255, 1)
|
||||
|
||||
self.advantage_fc = rn.NoisyLinear(255, 255)
|
||||
self.advantage_fc_norm = nn.LayerNorm(255)
|
||||
self.advantage = rn.NoisyLinear(255, action_size)
|
||||
self.advantage_fc = rn.NoisyLinear(255, 255)
|
||||
self.advantage_fc_norm = nn.LayerNorm(255)
|
||||
self.advantage = rn.NoisyLinear(255, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
|
||||
x = state_value + advantage - advantage.mean()
|
||||
return x
|
||||
x = state_value + advantage - advantage.mean()
|
||||
return x
|
||||
|
||||
#
|
||||
## Configuration
|
||||
|
|
@ -71,7 +71,7 @@ config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialSc
|
|||
#
|
||||
## Training Loop
|
||||
#
|
||||
def train(runner, agent, config, logger = None, logwriter = None):
|
||||
def train(runner, agent, config, logwriter=None):
|
||||
finished = False
|
||||
last_episode_num = 1
|
||||
while not finished:
|
||||
|
|
@ -79,56 +79,56 @@ def train(runner, agent, config, logger = None, logwriter = None):
|
|||
agent.learn()
|
||||
if logwriter is not None:
|
||||
if last_episode_num < runner.episode_num:
|
||||
last_episode_num = runner.episode_num
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(logger)
|
||||
last_episode_num = runner.episode_num
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(Logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end=" ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
# Logging
|
||||
logger = rltorch.log.Logger()
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
# Logging
|
||||
logwriter = None
|
||||
# logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN", logger = logger)
|
||||
target_net = rn.TargetNetwork(net, device = device)
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device=device, name="DQN")
|
||||
target_net = rn.TargetNetwork(net, device=device)
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = ArgMaxSelector(net, action_size, device = device)
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = ArgMaxSelector(net, action_size, device=device)
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity=config['memory_size'], alpha=config['prioritized_replay_sampling_priority'])
|
||||
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name="Training", memory=memory, logwriter=logwriter)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.DQNAgent(net, memory, config, target_net=target_net)
|
||||
|
||||
print("Training...")
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
print("Training...")
|
||||
train(runner, agent, config, logwriter=logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
print("Training Finished.")
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
|
|
|
|||
|
|
@ -9,58 +9,59 @@ import rltorch.env as E
|
|||
from rltorch.action_selector import ArgMaxSelector
|
||||
from tensorboardX import SummaryWriter
|
||||
import torch.multiprocessing as mp
|
||||
from rltorch.log import Logger
|
||||
|
||||
#
|
||||
## Networks
|
||||
#
|
||||
class Value(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.conv1 = nn.Conv2d(4, 32, kernel_size = (8, 8), stride = (4, 4))
|
||||
self.conv_norm1 = nn.LayerNorm([32, 19, 19])
|
||||
self.conv2 = nn.Conv2d(32, 64, kernel_size = (4, 4), stride = (2, 2))
|
||||
self.conv_norm2 = nn.LayerNorm([64, 8, 8])
|
||||
self.conv3 = nn.Conv2d(64, 64, kernel_size = (3, 3), stride = (1, 1))
|
||||
self.conv_norm3 = nn.LayerNorm([64, 6, 6])
|
||||
self.conv1 = nn.Conv2d(4, 32, kernel_size=(8, 8), stride=(4, 4))
|
||||
self.conv_norm1 = nn.LayerNorm([32, 19, 19])
|
||||
self.conv2 = nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2))
|
||||
self.conv_norm2 = nn.LayerNorm([64, 8, 8])
|
||||
self.conv3 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
|
||||
self.conv_norm3 = nn.LayerNorm([64, 6, 6])
|
||||
|
||||
self.fc1 = rn.NoisyLinear(64 * 6 * 6, 384)
|
||||
self.fc_norm = nn.LayerNorm(384)
|
||||
self.fc1 = rn.NoisyLinear(64 * 6 * 6, 384)
|
||||
self.fc_norm = nn.LayerNorm(384)
|
||||
|
||||
self.value_fc = rn.NoisyLinear(384, 384)
|
||||
self.value_fc_norm = nn.LayerNorm(384)
|
||||
self.value = rn.NoisyLinear(384, 1)
|
||||
self.value_fc = rn.NoisyLinear(384, 384)
|
||||
self.value_fc_norm = nn.LayerNorm(384)
|
||||
self.value = rn.NoisyLinear(384, 1)
|
||||
|
||||
self.advantage_fc = rn.NoisyLinear(384, 384)
|
||||
self.advantage_fc_norm = nn.LayerNorm(384)
|
||||
self.advantage = rn.NoisyLinear(384, action_size)
|
||||
self.advantage_fc = rn.NoisyLinear(384, 384)
|
||||
self.advantage_fc_norm = nn.LayerNorm(384)
|
||||
self.advantage = rn.NoisyLinear(384, action_size)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.conv_norm1(self.conv1(x)))
|
||||
x = F.relu(self.conv_norm2(self.conv2(x)))
|
||||
x = F.relu(self.conv_norm3(self.conv3(x)))
|
||||
def forward(self, x):
|
||||
x = F.relu(self.conv_norm1(self.conv1(x)))
|
||||
x = F.relu(self.conv_norm2(self.conv2(x)))
|
||||
x = F.relu(self.conv_norm3(self.conv3(x)))
|
||||
|
||||
# Makes batch_size dimension again
|
||||
x = x.view(-1, 64 * 6 * 6)
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
# Makes batch_size dimension again
|
||||
x = x.view(-1, 64 * 6 * 6)
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
|
||||
x = state_value + advantage - advantage.mean()
|
||||
x = state_value + advantage - advantage.mean()
|
||||
|
||||
# For debugging purposes...
|
||||
if torch.isnan(x).any().item():
|
||||
print("WARNING NAN IN MODEL DETECTED")
|
||||
|
||||
return x
|
||||
# For debugging purposes...
|
||||
if torch.isnan(x).any().item():
|
||||
print("WARNING NAN IN MODEL DETECTED")
|
||||
|
||||
return x
|
||||
|
||||
#
|
||||
## Configuration
|
||||
#
|
||||
|
|
@ -89,59 +90,73 @@ config['prioritized_replay_sampling_priority'] = 0.6
|
|||
# Should ideally start from 0 and move your way to 1 to prevent overfitting
|
||||
config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
|
||||
|
||||
#
|
||||
## Training Loop
|
||||
#
|
||||
def train(runner, agent, config, logwriter = None):
|
||||
finished = False
|
||||
while not finished:
|
||||
runner.run()
|
||||
agent.learn()
|
||||
if logwriter is not None:
|
||||
agent.value_net.log_named_parameters()
|
||||
agent.policy_net.log_named_parameters()
|
||||
logwriter.write(Logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# To not hit file descriptor memory limit
|
||||
torch.multiprocessing.set_sharing_strategy('file_system')
|
||||
# To not hit file descriptor memory limit
|
||||
torch.multiprocessing.set_sharing_strategy('file_system')
|
||||
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.FrameStack(E.TorchWrap(
|
||||
E.ProcessFrame(E.FireResetEnv(gym.make(config['environment_name'])),
|
||||
resize_shape = (80, 80), crop_bounds = [34, 194, 15, 145], grayscale = True))
|
||||
, 4)
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.FrameStack(E.TorchWrap(
|
||||
E.ProcessFrame(E.FireResetEnv(gym.make(config['environment_name'])),
|
||||
resize_shape=(80, 80), crop_bounds=[34, 194, 15, 145], grayscale=True))
|
||||
, 4)
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
# Logging
|
||||
logger = rltorch.log.Logger()
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
# Logging
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN")
|
||||
target_net = rn.TargetNetwork(net, device = device)
|
||||
net.model.share_memory()
|
||||
target_net.model.share_memory()
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device=device, name="DQN")
|
||||
target_net = rn.TargetNetwork(net, device=device)
|
||||
net.model.share_memory()
|
||||
target_net.model.share_memory()
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = ArgMaxSelector(net, action_size, device = device)
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = ArgMaxSelector(net, action_size, device=device)
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity=config['memory_size'], alpha=config['prioritized_replay_sampling_priority'])
|
||||
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.mp.EnvironmentRun(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.mp.EnvironmentRun(env, actor, config, name="Training", memory=memory, logwriter=logwriter)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.DQNAgent(net, memory, config, target_net=target_net)
|
||||
|
||||
print("Training...")
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
print("Training...")
|
||||
train(runner, agent, config, logwriter=logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# cProfile.run('train(runner, agent, config, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
runner.terminate() # We don't need the extra process anymore
|
||||
print("Training Finished.")
|
||||
runner.terminate() # We don't need the extra process anymore
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes=config['total_evaluation_episodes'], name="Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
|
|
|
|||
|
|
@ -2,14 +2,14 @@ from copy import deepcopy
|
|||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import rltorch.log as log
|
||||
|
||||
class A2CSingleAgent:
|
||||
def __init__(self, policy_net, value_net, memory, config, logger=None):
|
||||
def __init__(self, policy_net, value_net, memory, config):
|
||||
self.policy_net = policy_net
|
||||
self.value_net = value_net
|
||||
self.memory = memory
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
|
||||
def _discount_rewards(self, rewards):
|
||||
gammas = torch.ones_like(rewards)
|
||||
|
|
@ -79,9 +79,9 @@ class A2CSingleAgent:
|
|||
|
||||
policy_loss = (-log_prob_batch * advantages).sum()
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append("Loss/Policy", policy_loss.item())
|
||||
self.logger.append("Loss/Value", value_loss.item())
|
||||
if log.enabled:
|
||||
log.Logger["Loss/Policy"].append(policy_loss.item())
|
||||
log.Logger["Loss/Value"].append(value_loss.item())
|
||||
|
||||
|
||||
self.policy_net.zero_grad()
|
||||
|
|
|
|||
|
|
@ -3,14 +3,14 @@ from copy import deepcopy
|
|||
import rltorch.memory as M
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import rltorch.log as log
|
||||
|
||||
class DQNAgent:
|
||||
def __init__(self, net, memory, config, target_net=None, logger=None):
|
||||
def __init__(self, net, memory, config, target_net=None):
|
||||
self.net = net
|
||||
self.target_net = target_net
|
||||
self.memory = memory
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
def save(self, file_location):
|
||||
torch.save(self.net.model.state_dict(), file_location)
|
||||
def load(self, file_location):
|
||||
|
|
@ -18,7 +18,7 @@ class DQNAgent:
|
|||
self.net.model.to(self.net.device)
|
||||
self.target_net.sync()
|
||||
|
||||
def learn(self, logger=None):
|
||||
def learn(self):
|
||||
if len(self.memory) < self.config['batch_size']:
|
||||
return
|
||||
|
||||
|
|
@ -68,8 +68,8 @@ class DQNAgent:
|
|||
# loss = F.smooth_l1_loss(obtained_values, expected_values)
|
||||
loss = F.mse_loss(obtained_values, expected_values)
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append("Loss", loss.item())
|
||||
if log.enabled:
|
||||
log.Logger["Loss"].append(loss.item())
|
||||
|
||||
self.net.zero_grad()
|
||||
loss.backward()
|
||||
|
|
|
|||
|
|
@ -3,15 +3,14 @@ from copy import deepcopy
|
|||
import rltorch.memory as M
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
import rltorch.log as log
|
||||
|
||||
class DQfDAgent:
|
||||
def __init__(self, net, memory, config, target_net=None, logger=None):
|
||||
def __init__(self, net, memory, config, target_net=None):
|
||||
self.net = net
|
||||
self.target_net = target_net
|
||||
self.memory = memory
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
def save(self, file_location):
|
||||
torch.save(self.net.model.state_dict(), file_location)
|
||||
def load(self, file_location):
|
||||
|
|
@ -19,7 +18,7 @@ class DQfDAgent:
|
|||
self.net.model.to(self.net.device)
|
||||
self.target_net.sync()
|
||||
|
||||
def learn(self, logger=None):
|
||||
def learn(self):
|
||||
if len(self.memory) < self.config['batch_size']:
|
||||
return
|
||||
|
||||
|
|
@ -149,8 +148,8 @@ class DQfDAgent:
|
|||
demo_loss = 0
|
||||
loss = td_importance * dqn_loss + td_importance * dqn_n_step_loss + demo_importance * demo_loss
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append("Loss", loss.item())
|
||||
if log.enabled:
|
||||
log.Logger["Loss"].append(loss.item())
|
||||
|
||||
self.net.zero_grad()
|
||||
loss.backward()
|
||||
|
|
|
|||
|
|
@ -3,15 +3,15 @@ import torch
|
|||
import torch.nn.functional as F
|
||||
from torch.distributions import Categorical
|
||||
import rltorch
|
||||
import rltorch.log as log
|
||||
|
||||
class PPOAgent:
|
||||
def __init__(self, policy_net, value_net, memory, config, logger=None):
|
||||
def __init__(self, policy_net, value_net, memory, config):
|
||||
self.policy_net = policy_net
|
||||
self.old_policy_net = rltorch.network.TargetNetwork(policy_net)
|
||||
self.value_net = value_net
|
||||
self.memory = memory
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
|
||||
def _discount_rewards(self, rewards):
|
||||
gammas = torch.ones_like(rewards)
|
||||
|
|
@ -59,9 +59,9 @@ class PPOAgent:
|
|||
policy_loss2 = policy_ratio.clamp(min=0.8, max=1.2) * advantages # From original paper
|
||||
policy_loss = -torch.min(policy_loss1, policy_loss2).sum()
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append("Loss/Policy", policy_loss.item())
|
||||
self.logger.append("Loss/Value", value_loss.item())
|
||||
if log.enabled:
|
||||
log.Logger["Loss/Policy"].append(policy_loss.item())
|
||||
log.Logger["Loss/Value"].append(value_loss.item())
|
||||
|
||||
self.old_policy_net.sync()
|
||||
self.policy_net.zero_grad()
|
||||
|
|
|
|||
|
|
@ -6,13 +6,14 @@ import torch.nn.functional as F
|
|||
from torch.distributions import Categorical
|
||||
import rltorch
|
||||
import rltorch.memory as M
|
||||
import rltorch.log as log
|
||||
|
||||
|
||||
# Q-Evolutionary Policy Agent
|
||||
# Maximizes the policy with respect to the Q-Value function.
|
||||
# Since function is non-differentiabile, depends on the Evolutionary Strategy algorithm
|
||||
class QEPAgent:
|
||||
def __init__(self, policy_net, value_net, memory, config, target_value_net=None, logger=None, entropy_importance=0, policy_skip=4):
|
||||
def __init__(self, policy_net, value_net, memory, config, target_value_net=None, entropy_importance=0, policy_skip=4):
|
||||
self.policy_net = policy_net
|
||||
assert isinstance(self.policy_net, rltorch.network.ESNetwork) or isinstance(self.policy_net, rltorch.network.ESNetworkMP)
|
||||
self.policy_net.fitness = self.fitness
|
||||
|
|
@ -20,7 +21,6 @@ class QEPAgent:
|
|||
self.target_value_net = target_value_net
|
||||
self.memory = memory
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
self.policy_skip = policy_skip
|
||||
self.entropy_importance = entropy_importance
|
||||
|
||||
|
|
@ -67,7 +67,7 @@ class QEPAgent:
|
|||
return (entropy_importance * entropy_loss - value_importance * obtained_values).mean().item()
|
||||
|
||||
|
||||
def learn(self, logger=None):
|
||||
def learn(self):
|
||||
if len(self.memory) < self.config['batch_size']:
|
||||
return
|
||||
|
||||
|
|
@ -114,8 +114,8 @@ class QEPAgent:
|
|||
else:
|
||||
value_loss = F.mse_loss(obtained_values, expected_values)
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append("Loss/Value", value_loss.item())
|
||||
if log.enabled:
|
||||
log.Logger["Loss/Value"].append(value_loss.item())
|
||||
|
||||
self.value_net.zero_grad()
|
||||
value_loss.backward()
|
||||
|
|
|
|||
|
|
@ -4,14 +4,13 @@ import torch
|
|||
import rltorch
|
||||
|
||||
class REINFORCEAgent:
|
||||
def __init__(self, net, memory, config, target_net=None, logger=None):
|
||||
def __init__(self, net, memory, config, target_net=None):
|
||||
self.net = net
|
||||
if not isinstance(memory, rltorch.memory.EpisodeMemory):
|
||||
raise ValueError("Memory must be of instance EpisodeMemory")
|
||||
self.memory = memory
|
||||
self.config = deepcopy(config)
|
||||
self.target_net = target_net
|
||||
self.logger = logger
|
||||
|
||||
# Shaped rewards implements three improvements to REINFORCE
|
||||
# 1) Discounted rewards, future rewards matter less than current
|
||||
|
|
@ -42,8 +41,8 @@ class REINFORCEAgent:
|
|||
|
||||
policy_loss = (-log_prob_batch * shaped_reward_batch).sum()
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append("Loss", policy_loss.item())
|
||||
if rltorch.log.enabled:
|
||||
rltorch.log.Logger["Loss"].append(policy_loss.item())
|
||||
|
||||
self.net.zero_grad()
|
||||
policy_loss.backward()
|
||||
|
|
|
|||
19
rltorch/env/simulate.py
vendored
19
rltorch/env/simulate.py
vendored
|
|
@ -2,7 +2,7 @@ from copy import deepcopy
|
|||
import time
|
||||
import rltorch
|
||||
|
||||
def simulateEnvEps(env, actor, config, total_episodes=1, memory=None, logger=None, name="", render=False):
|
||||
def simulateEnvEps(env, actor, config, total_episodes=1, memory=None, name="", render=False):
|
||||
for episode in range(total_episodes):
|
||||
state = env.reset()
|
||||
done = False
|
||||
|
|
@ -23,8 +23,8 @@ def simulateEnvEps(env, actor, config, total_episodes=1, memory=None, logger=Non
|
|||
print("episode: {}/{}, score: {}"
|
||||
.format(episode, total_episodes, episode_reward), flush=True)
|
||||
|
||||
if logger is not None:
|
||||
logger.append(name + '/EpisodeReward', episode_reward)
|
||||
if rltorch.log.enabled:
|
||||
rltorch.log.Logger[name + '/EpisodeReward'].append(episode_reward)
|
||||
|
||||
|
||||
class EnvironmentRunSync:
|
||||
|
|
@ -42,7 +42,6 @@ class EnvironmentRunSync:
|
|||
|
||||
def run(self, iterations):
|
||||
state = self.last_state
|
||||
logger = rltorch.log.Logger() if self.logwriter is not None else None
|
||||
for _ in range(iterations):
|
||||
action = self.actor.act(state)
|
||||
next_state, reward, done, _ = self.env.step(action)
|
||||
|
|
@ -61,13 +60,13 @@ class EnvironmentRunSync:
|
|||
.format(self.episode_num, self.config['total_training_episodes'], self.episode_reward), flush=True)
|
||||
|
||||
if self.logwriter is not None:
|
||||
logger.append(self.name + '/EpisodeReward', self.episode_reward)
|
||||
rltorch.log.Logger[self.name + '/EpisodeReward'].append(self.episode_reward)
|
||||
self.episode_reward = 0
|
||||
state = self.env.reset()
|
||||
self.episode_num += 1
|
||||
|
||||
if self.logwriter is not None:
|
||||
self.logwriter.write(logger)
|
||||
self.logwriter.write(rltorch.log.Logger)
|
||||
|
||||
self.last_state = state
|
||||
|
||||
|
|
@ -86,15 +85,13 @@ class EnvironmentEpisodeSync:
|
|||
state = self.env.reset()
|
||||
done = False
|
||||
episodeReward = 0
|
||||
logger = rltorch.log.Logger() if self.logwriter is not None else None
|
||||
while not done:
|
||||
action = self.actor.act(state)
|
||||
next_state, reward, done, _ = self.env.step(action)
|
||||
|
||||
episodeReward += reward
|
||||
if self.memory is not None:
|
||||
self.memory.append(state, action, reward, next_state, done)
|
||||
|
||||
|
||||
state = next_state
|
||||
|
||||
if self.episode_num % self.config['print_stat_n_eps'] == 0:
|
||||
|
|
@ -102,7 +99,7 @@ class EnvironmentEpisodeSync:
|
|||
.format(self.episode_num, self.config['total_training_episodes'], episodeReward), flush=True)
|
||||
|
||||
if self.logwriter is not None:
|
||||
logger.append(self.name + '/EpisodeReward', episodeReward)
|
||||
self.logwriter.write(logger)
|
||||
rltorch.log.Logger[self.name + '/EpisodeReward'].append(episodeReward)
|
||||
self.logwriter.write(rltorch.log.Logger)
|
||||
|
||||
self.episode_num += 1
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ from typing import Dict, List, Any
|
|||
import numpy as np
|
||||
import torch
|
||||
|
||||
enabled = False
|
||||
Logger: Dict[Any, List[Any]] = defaultdict(list)
|
||||
|
||||
class LogWriter:
|
||||
|
|
|
|||
|
|
@ -2,4 +2,3 @@ from .EpisodeMemory import *
|
|||
from .ReplayMemory import *
|
||||
from .PrioritizedReplayMemory import *
|
||||
from .DQfDMemory import *
|
||||
from .iDQfDMemory import *
|
||||
|
|
@ -3,14 +3,14 @@
|
|||
|
||||
from copy import deepcopy
|
||||
import torch.multiprocessing as mp
|
||||
import rltorch.log as log
|
||||
|
||||
class EnvironmentEpisode(mp.Process):
|
||||
def __init__(self, env, actor, config, logger=None, name=""):
|
||||
def __init__(self, env, actor, config, name=""):
|
||||
super(EnvironmentEpisode, self).__init__()
|
||||
self.env = env
|
||||
self.actor = actor
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
self.name = name
|
||||
self.episode_num = 1
|
||||
|
||||
|
|
@ -30,7 +30,7 @@ class EnvironmentEpisode(mp.Process):
|
|||
if printstat:
|
||||
print("episode: {}/{}, score: {}"
|
||||
.format(self.episode_num, self.config['total_training_episodes'], episode_reward))
|
||||
if self.logger is not None:
|
||||
self.logger.append(self.name + '/EpisodeReward', episode_reward)
|
||||
if log.enabled:
|
||||
log.Logger[self.name + '/EpisodeReward'].append(episode_reward)
|
||||
|
||||
self.episode_num += 1
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from copy import deepcopy
|
|||
import numpy as np
|
||||
import torch
|
||||
from .Network import Network
|
||||
|
||||
import rltorch.log as log
|
||||
|
||||
# [TODO] Should we torch.no_grad the __call__?
|
||||
# What if we want to sometimes do gradient descent as well?
|
||||
|
|
@ -34,13 +34,11 @@ class ESNetwork(Network):
|
|||
A dictionary of configuration items.
|
||||
device
|
||||
A device to send the weights to.
|
||||
logger
|
||||
Keeps track of historical weights
|
||||
name
|
||||
For use in logger to differentiate in analysis.
|
||||
"""
|
||||
def __init__(self, model, optimizer, population_size, fitness_fn, config, sigma=0.05, device=None, logger=None, name=""):
|
||||
super(ESNetwork, self).__init__(model, optimizer, config, device, logger, name)
|
||||
def __init__(self, model, optimizer, population_size, fitness_fn, config, sigma=0.05, device=None, name=""):
|
||||
super(ESNetwork, self).__init__(model, optimizer, config, device, name)
|
||||
self.population_size = population_size
|
||||
self.fitness = fitness_fn
|
||||
self.sigma = sigma
|
||||
|
|
@ -105,8 +103,8 @@ class ESNetwork(Network):
|
|||
[self.fitness(x, *args) for x in candidate_solutions],
|
||||
device=self.device
|
||||
)
|
||||
if self.logger is not None:
|
||||
self.logger.append(self.name + "/" + "fitness_value", fitness_values.mean().item())
|
||||
if log.enabled:
|
||||
log.Logger[self.name + "/" + "fitness_value"].append(fitness_values.mean().item())
|
||||
fitness_values = (fitness_values - fitness_values.mean()) / (fitness_values.std() + np.finfo('float').eps)
|
||||
|
||||
## Insert adjustments into gradients slot
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import numpy as np
|
|||
import torch
|
||||
import torch.multiprocessing as mp
|
||||
from .Network import Network
|
||||
import rltorch.log as log
|
||||
|
||||
class fn_copy:
|
||||
def __init__(self, fn, args):
|
||||
|
|
@ -19,8 +20,8 @@ class ESNetworkMP(Network):
|
|||
fitness_fun := model, *args -> fitness_value (float)
|
||||
We wish to find a model that maximizes the fitness function
|
||||
"""
|
||||
def __init__(self, model, optimizer, population_size, fitness_fn, config, sigma=0.05, device=None, logger=None, name=""):
|
||||
super(ESNetworkMP, self).__init__(model, optimizer, config, device, logger, name)
|
||||
def __init__(self, model, optimizer, population_size, fitness_fn, config, sigma=0.05, device=None, name=""):
|
||||
super(ESNetworkMP, self).__init__(model, optimizer, config, device, name)
|
||||
self.population_size = population_size
|
||||
self.fitness = fitness_fn
|
||||
self.sigma = sigma
|
||||
|
|
@ -76,8 +77,8 @@ class ESNetworkMP(Network):
|
|||
device=self.device
|
||||
)
|
||||
|
||||
if self.logger is not None:
|
||||
self.logger.append(self.name + "/" + "fitness_value", fitness_values.mean().item())
|
||||
if log.enabled:
|
||||
log.Logger[self.name + "/" + "fitness_value"].append(fitness_values.mean().item())
|
||||
fitness_values = (fitness_values - fitness_values.mean()) / (fitness_values.std() + np.finfo('float').eps)
|
||||
|
||||
## Insert adjustments into gradients slot
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
import rltorch.log as log
|
||||
|
||||
class Network:
|
||||
"""
|
||||
Wrapper around model and optimizer in PyTorch to abstract away common use cases.
|
||||
|
|
@ -12,12 +14,10 @@ class Network:
|
|||
A dictionary of configuration items.
|
||||
device
|
||||
A device to send the weights to.
|
||||
logger
|
||||
Keeps track of historical weights
|
||||
name
|
||||
For use in logger to differentiate in analysis.
|
||||
"""
|
||||
def __init__(self, model, optimizer, config, device=None, logger=None, name=""):
|
||||
def __init__(self, model, optimizer, config, device=None, name=""):
|
||||
self.model = model
|
||||
if 'weight_decay' in config:
|
||||
self.optimizer = optimizer(
|
||||
|
|
@ -27,7 +27,6 @@ class Network:
|
|||
)
|
||||
else:
|
||||
self.optimizer = optimizer(model.parameters(), lr=config['learning_rate'])
|
||||
self.logger = logger
|
||||
self.name = name
|
||||
self.device = device
|
||||
if self.device is not None:
|
||||
|
|
@ -63,8 +62,8 @@ class Network:
|
|||
self.optimizer.step()
|
||||
|
||||
def log_named_parameters(self):
|
||||
if self.logger is not None:
|
||||
if log.enabled:
|
||||
for name, param in self.model.named_parameters():
|
||||
self.logger.append(self.name + "/" + name, param.cpu().detach().numpy())
|
||||
log.Logger[self.name + "/" + name].append(param.cpu().detach().numpy())
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue