Fixed parallel implementation of getting experiences by using a queue
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parent
5094ed53af
commit
115543d201
4 changed files with 33 additions and 22 deletions
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@ -9,14 +9,14 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import ArgMaxSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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class Value(nn.Module):
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def __init__(self, state_size, action_size):
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super(Value, 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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@ -28,7 +28,6 @@ class Value(nn.Module):
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self.advantage_fc_norm = nn.LayerNorm(64)
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self.advantage = 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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@ -67,13 +66,16 @@ config['prioritized_replay_sampling_priority'] = 0.6
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# Should ideally start from 0 and move your way to 1 to prevent overfitting
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config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
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def train(runner, agent, config, logwriter = None):
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def train(runner, agent, config, logwriter = None, memory = None):
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finished = False
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episode_num = 1
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memory_queue = mp.Queue(maxsize = config['replay_skip'] + 1)
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while not finished:
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runner.run(config['replay_skip'] + 1, printstat = runner.episode_num % config['print_stat_n_eps'] == 0)
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runner.run(config['replay_skip'] + 1, printstat = runner.episode_num % config['print_stat_n_eps'] == 0, memory = memory_queue)
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agent.learn()
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runner.join()
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for i in range(config['replay_skip'] + 1):
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memory.append(*memory_queue.get())
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# When the episode number changes, write out the weight histograms
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if logwriter is not None and episode_num < runner.episode_num:
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episode_num = runner.episode_num
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@ -84,6 +86,7 @@ def train(runner, agent, config, logwriter = None):
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finished = runner.episode_num > config['total_training_episodes']
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torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
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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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@ -98,11 +101,14 @@ action_size = env.action_space.n
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logger = rltorch.log.Logger()
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logwriter = rltorch.log.LogWriter(logger, 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.Network(Value(state_size, action_size),
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torch.optim.Adam, config, device = device, logger = logger, name = "DQN")
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target_net = rn.TargetNetwork(net, device = device)
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net.model.share_memory()
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target_net.model.share_memory()
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# Actor takes a net and uses it to produce actions from given states
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actor = ArgMaxSelector(net, action_size, device = device)
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@ -111,14 +117,14 @@ memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = con
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# memory = M.ReplayMemory(capacity = config['memory_size'])
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# Runner performs a certain number of steps in the environment
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runner = rltorch.mp.EnvironmentRun(env, actor, config, memory = memory, logger = logger, name = "Training")
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runner = rltorch.mp.EnvironmentRun(env, actor, config, logger = logger, name = "Training")
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runner.start()
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# Agent is what performs the training
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agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
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print("Training...")
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train(runner, agent, config, logwriter = logwriter)
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train(runner, agent, config, logwriter = logwriter, memory = memory)
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# For profiling...
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# import cProfile
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@ -132,4 +138,4 @@ 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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@ -9,6 +9,7 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import ArgMaxSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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class Value(nn.Module):
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def __init__(self, state_size, action_size):
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@ -87,13 +88,16 @@ config['prioritized_replay_sampling_priority'] = 0.6
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# Should ideally start from 0 and move your way to 1 to prevent overfitting
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config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
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def train(runner, agent, config, logwriter = None):
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def train(runner, agent, config, logwriter = None, memory = None):
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finished = False
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episode_num = 1
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memory_queue = mp.Queue(maxsize = config['replay_skip'] + 1)
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while not finished:
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runner.run(config['replay_skip'] + 1, printstat = runner.episode_num % config['print_stat_n_eps'] == 0)
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runner.run(config['replay_skip'] + 1, printstat = runner.episode_num % config['print_stat_n_eps'] == 0, memory = memory_queue)
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agent.learn()
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runner.join()
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for i in range(config['replay_skip'] + 1):
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memory.append(*memory_queue.get())
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# When the episode number changes, write out the weight histograms
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if logwriter is not None and episode_num < runner.episode_num:
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episode_num = runner.episode_num
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@ -104,6 +108,7 @@ def train(runner, agent, config, logwriter = None):
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finished = runner.episode_num > config['total_training_episodes']
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torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end = " ")
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env = E.FrameStack(E.TorchWrap(
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@ -125,6 +130,8 @@ device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disa
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net = rn.Network(Value(state_size, action_size),
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torch.optim.Adam, config, device = device, logger = logger, name = "DQN")
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target_net = rn.TargetNetwork(net, device = device)
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net.model.share_memory()
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target_net.model.share_memory()
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# Actor takes a network and uses it to produce actions from given states
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actor = ArgMaxSelector(net, action_size, device = device)
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@ -132,14 +139,14 @@ actor = ArgMaxSelector(net, action_size, device = device)
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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# Runner performs a certain number of steps in the environment
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runner = rltorch.mp.EnvironmentRun(env, actor, config, memory = memory, logger = logger, name = "Training")
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runner = rltorch.mp.EnvironmentRun(env, actor, config, logger = logger, name = "Training")
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runner.start()
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# Agent is what performs the training
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agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
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print("Training...")
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train(runner, agent, config, logwriter = logwriter)
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train(runner, agent, config, logwriter = logwriter, memory = memory)
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# For profiling...
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# import cProfile
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@ -2,17 +2,16 @@ from copy import deepcopy
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import torch.multiprocessing as mp
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class EnvironmentEpisode(mp.Process):
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def __init__(self, env, actor, config, memory = None, logger = None, name = ""):
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def __init__(self, env, actor, config, logger = None, name = ""):
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super(EnvironmentEpisode, self).__init__()
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self.env = env
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self.actor = actor
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self.memory = memory
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self.config = deepcopy(config)
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self.logger = logger
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self.name = name
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self.episode_num = 1
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def run(self, printstat = False):
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def run(self, printstat = False, memory = None):
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state = self.env.reset()
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done = False
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episode_reward = 0
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@ -21,8 +20,8 @@ class EnvironmentEpisode(mp.Process):
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next_state, reward, done, _ = self.env.step(action)
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episode_reward = episode_reward + reward
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if self.memory is not None:
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self.memory.append(state, action, reward, next_state, done)
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if memory is not None:
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memory.put((state, action, reward, next_state, done))
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state = next_state
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if printstat:
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@ -2,11 +2,10 @@ from copy import deepcopy
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import torch.multiprocessing as mp
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class EnvironmentRun(mp.Process):
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def __init__(self, env, actor, config, memory = None, logger = None, name = ""):
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def __init__(self, env, actor, config, logger = None, name = ""):
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super(EnvironmentRun, self).__init__()
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self.env = env
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self.actor = actor
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self.memory = memory
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self.config = deepcopy(config)
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self.logger = logger
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self.name = name
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@ -14,15 +13,15 @@ class EnvironmentRun(mp.Process):
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self.episode_reward = 0
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self.last_state = env.reset()
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def run(self, iterations = 1, printstat = False):
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def run(self, iterations = 1, printstat = False, memory = None):
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state = self.last_state
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for _ in range(iterations):
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action = self.actor.act(state)
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next_state, reward, done, _ = self.env.step(action)
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self.episode_reward = self.episode_reward + reward
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if self.memory is not None:
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self.memory.append(state, action, reward, next_state, done)
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if memory is not None:
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memory.put((state, action, reward, next_state, done))
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state = next_state
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if done:
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