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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@ -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
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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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# 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
|
||||
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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue