Fixed EnvironmentRun to be properly multiprocess.
Fixed the prioirity of bad states to be the smallest [TODO] Make EnvironmentEpisode properly multiprocess
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parent
115543d201
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460d4c05c1
8 changed files with 288 additions and 164 deletions
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@ -65,77 +65,77 @@ config['prioritized_replay_sampling_priority'] = 0.6
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# 1 - Lower the importance of high losses
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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, memory = None):
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def train(runner, agent, config, logger = None, logwriter = 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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last_episode_num = 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, memory = memory_queue)
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runner.run()
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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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agent.net.log_named_parameters()
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if logwriter is not None:
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logwriter.write()
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finished = runner.episode_num > config['total_training_episodes']
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# When the episode number changes, log network paramters
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with runner.episode_num.get_lock():
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if logwriter is not None and last_episode_num < runner.episode_num.value:
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last_episode_num = runner.episode_num.value
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agent.net.log_named_parameters()
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if logwriter is not None:
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logwriter.write(logger)
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finished = runner.episode_num.value > 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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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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if __name__ == "__main__":
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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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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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# Logging
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logger = rltorch.log.Logger()
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# logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
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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.Network(Value(state_size, action_size),
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torch.optim.Adam, config, device = device, 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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# Memory stores experiences for later training
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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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, name = "Training", memory = memory, logwriter = logwriter)
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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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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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print("Training...")
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# Logging
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logger = rltorch.log.Logger()
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logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
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train(runner, agent, 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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# 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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print("Training Finished.")
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runner.terminate() # We don't need the extra process anymore
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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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# Memory stores experiences for later training
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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# memory = M.ReplayMemory(capacity = config['memory_size'])
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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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# Runner performs a certain number of steps in the environment
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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, memory = memory)
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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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runner.terminate() # We don't need the extra process anymore
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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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114
examples/pong.py
114
examples/pong.py
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@ -88,76 +88,60 @@ 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, 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, 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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agent.net.log_named_parameters()
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if logwriter is not None:
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logwriter.write()
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finished = runner.episode_num > config['total_training_episodes']
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if __name__ == "__main__":
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torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
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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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# 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.FrameStack(E.TorchWrap(
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E.ProcessFrame(E.FireResetEnv(gym.make(config['environment_name'])),
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resize_shape = (80, 80), crop_bounds = [34, 194, 15, 145], grayscale = True))
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, 4)
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env.seed(config['seed'])
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print("Done.")
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resize_shape = (80, 80), crop_bounds = [34, 194, 15, 145], grayscale = True))
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, 4)
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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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# 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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net = rn.Network(Value(state_size, action_size),
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torch.optim.Adam, config, device = device, 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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# Memory stores experiences for later training
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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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, name = "Training", memory = memory, logwriter = logwriter)
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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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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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print("Training...")
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# Logging
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logger = rltorch.log.Logger()
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logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
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train(runner, agent, config, logger = logger, logwriter = logwriter)
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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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# 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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# 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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# Memory stores experiences for later training
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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print("Training Finished.")
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runner.terminate() # We don't need the extra process anymore
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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, logger = logger, name = "Training")
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runner.start()
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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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# 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, memory = memory)
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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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runner.terminate() # We don't need the extra process anymore
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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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@ -13,7 +13,7 @@ class DQNAgent:
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self.config = deepcopy(config)
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self.logger = logger
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def learn(self):
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def learn(self, logger = None):
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if len(self.memory) < self.config['batch_size']:
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return
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@ -9,6 +9,8 @@ class Logger:
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if tag not in self.log.keys():
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self.log[tag] = []
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self.log[tag].append(value)
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def clear(self):
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self.log.clear()
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def keys(self):
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return self.log.keys()
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def __len__(self):
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@ -25,20 +27,37 @@ class Logger:
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return reversed(self.log)
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# Workaround since we can't use SummaryWriter in a different process
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# class LogWriter:
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# def __init__(self, logger, writer):
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# self.logger = logger
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# self.writer = writer
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# self.steps = Counter()
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# def write(self):
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# for key in self.logger.keys():
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# for value in self.logger[key]:
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# self.steps[key] += 1
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# if isinstance(value, int) or isinstance(value, float):
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# self.writer.add_scalar(key, value, self.steps[key])
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# if isinstance(value, np.ndarray) or isinstance(value, torch.Tensor):
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# self.writer.add_histogram(key, value, self.steps[key])
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# self.logger.log = {}
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# def close(self):
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# self.writer.close()
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class LogWriter:
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def __init__(self, logger, writer):
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self.logger = logger
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def __init__(self, writer):
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self.writer = writer
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self.steps = Counter()
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def write(self):
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for key in self.logger.keys():
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for value in self.logger[key]:
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def write(self, logger):
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for key in logger.keys():
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for value in logger[key]:
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self.steps[key] += 1
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if isinstance(value, int) or isinstance(value, float):
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self.writer.add_scalar(key, value, self.steps[key])
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if isinstance(value, np.ndarray) or isinstance(value, torch.Tensor):
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self.writer.add_histogram(key, value, self.steps[key])
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self.logger.log = {}
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logger.clear()
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def close(self):
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self.writer.close()
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@ -246,7 +246,8 @@ class PrioritizedReplayMemory(ReplayMemory):
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assert len(idxes) == len(priorities)
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priorities += np.finfo('float').eps
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for idx, priority in zip(idxes, priorities):
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assert priority > 0
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if priority < 0:
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priority = np.finfo('float').eps
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assert 0 <= idx < len(self.memory)
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self._it_sum[idx] = priority ** self._alpha
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self._it_min[idx] = priority ** self._alpha
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@ -1,3 +1,6 @@
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# EnvironmentEpisode is currently under maintenance
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# Feel free to use the old API, though it is scheduled to change soon.
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from copy import deepcopy
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import torch.multiprocessing as mp
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@ -32,3 +35,85 @@ class EnvironmentEpisode(mp.Process):
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self.episode_num += 1
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# from copy import deepcopy
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# import torch.multiprocessing as mp
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# from ctypes import *
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# import rltorch.log
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# def envepisode(actor, env, episode_num, config, runcondition, memoryqueue = None, logqueue = None, name = ""):
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# # Wait for signal to start running through the environment
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# while runcondition.wait():
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# # Start a logger to log the rewards
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# logger = rltorch.log.Logger()
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# state = env.reset()
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# episode_reward = 0
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# done = False
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# while not done:
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# action = actor.act(state)
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# next_state, reward, done, _ = env.step(action)
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# episode_reward += reward
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# if memoryqueue is not None:
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# memoryqueue.put((state, action, reward, next_state, done))
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# state = next_state
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# if done:
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# with episode_num.get_lock():
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# if episode_num.value % config['print_stat_n_eps'] == 0:
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# print("episode: {}/{}, score: {}"
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# .format(episode_num.value, config['total_training_episodes'], episode_reward))
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# if logger is not None:
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# logger.append(name + '/EpisodeReward', episode_reward)
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# episode_reward = 0
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# state = env.reset()
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# with episode_num.get_lock():
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# episode_num.value += 1
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# logqueue.put(logger)
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# class EnvironmentRun():
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# def __init__(self, env_func, actor, config, memory = None, name = ""):
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# self.config = deepcopy(config)
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# self.memory = memory
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# self.episode_num = mp.Value(c_uint)
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# self.runcondition = mp.Event()
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# # Interestingly enough, there isn't a good reliable way to know how many states an episode will have
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# # Perhaps we can share a uint to keep track...
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# self.memory_queue = mp.Queue(maxsize = config['replay_skip'] + 1)
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# self.logqueue = mp.Queue(maxsize = 1)
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# with self.episode_num.get_lock():
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# self.episode_num.value = 1
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# self.runner = mp.Process(target=envrun,
|
||||
# args=(actor, env_func, self.episode_num, config, self.runcondition),
|
||||
# kwargs = {'iterations': config['replay_skip'] + 1,
|
||||
# 'memoryqueue' : self.memory_queue, 'logqueue' : self.logqueue, 'name' : name})
|
||||
# self.runner.start()
|
||||
|
||||
# def run(self):
|
||||
# self.runcondition.set()
|
||||
|
||||
# def join(self):
|
||||
# self._sync_memory()
|
||||
# if self.logwriter is not None:
|
||||
# self.logwriter.write(self._get_reward_logger())
|
||||
|
||||
# def sync_memory(self):
|
||||
# if self.memory is not None:
|
||||
# for i in range(self.config['replay_skip'] + 1):
|
||||
# self.memory.append(*self.memory_queue.get())
|
||||
|
||||
# def get_reward_logger(self):
|
||||
# return self.logqueue.get()
|
||||
|
||||
# def terminate(self):
|
||||
# self.runner.terminate()
|
||||
|
||||
|
|
|
|||
|
|
@ -1,38 +1,73 @@
|
|||
from copy import deepcopy
|
||||
import torch.multiprocessing as mp
|
||||
from ctypes import *
|
||||
import rltorch.log
|
||||
|
||||
class EnvironmentRun(mp.Process):
|
||||
def __init__(self, env, actor, config, logger = None, name = ""):
|
||||
super(EnvironmentRun, self).__init__()
|
||||
self.env = env
|
||||
self.actor = actor
|
||||
self.config = deepcopy(config)
|
||||
self.logger = logger
|
||||
self.name = name
|
||||
self.episode_num = 1
|
||||
self.episode_reward = 0
|
||||
self.last_state = env.reset()
|
||||
|
||||
def run(self, iterations = 1, printstat = False, memory = None):
|
||||
state = self.last_state
|
||||
def envrun(actor, env, episode_num, config, runcondition, iterations = 1, memoryqueue = None, logqueue = None, name = ""):
|
||||
state = env.reset()
|
||||
episode_reward = 0
|
||||
# Wait for signal to start running through the environment
|
||||
while runcondition.wait():
|
||||
# Start a logger to log the rewards
|
||||
logger = rltorch.log.Logger()
|
||||
for _ in range(iterations):
|
||||
action = self.actor.act(state)
|
||||
next_state, reward, done, _ = self.env.step(action)
|
||||
|
||||
self.episode_reward = self.episode_reward + reward
|
||||
if memory is not None:
|
||||
memory.put((state, action, reward, next_state, done))
|
||||
action = actor.act(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
|
||||
episode_reward += reward
|
||||
if memoryqueue is not None:
|
||||
memoryqueue.put((state, action, reward, next_state, done))
|
||||
|
||||
state = next_state
|
||||
|
||||
if done:
|
||||
if printstat:
|
||||
with episode_num.get_lock():
|
||||
if episode_num.value % config['print_stat_n_eps'] == 0:
|
||||
print("episode: {}/{}, score: {}"
|
||||
.format(self.episode_num, self.config['total_training_episodes'], self.episode_reward))
|
||||
if self.logger is not None:
|
||||
self.logger.append(self.name + '/EpisodeReward', self.episode_reward)
|
||||
self.episode_num = self.episode_num + 1
|
||||
self.episode_reward = 0
|
||||
state = self.env.reset()
|
||||
.format(episode_num.value, config['total_training_episodes'], episode_reward))
|
||||
|
||||
if logger is not None:
|
||||
logger.append(name + '/EpisodeReward', episode_reward)
|
||||
episode_reward = 0
|
||||
state = env.reset()
|
||||
with episode_num.get_lock():
|
||||
episode_num.value += 1
|
||||
|
||||
logqueue.put(logger)
|
||||
|
||||
self.last_state = state
|
||||
class EnvironmentRun():
|
||||
def __init__(self, env, actor, config, memory = None, logwriter = None, name = ""):
|
||||
self.config = deepcopy(config)
|
||||
self.logwriter = logwriter
|
||||
self.memory = memory
|
||||
self.episode_num = mp.Value(c_uint)
|
||||
self.runcondition = mp.Event()
|
||||
self.memory_queue = mp.Queue(maxsize = config['replay_skip'] + 1)
|
||||
self.logqueue = mp.Queue(maxsize = 1)
|
||||
with self.episode_num.get_lock():
|
||||
self.episode_num.value = 1
|
||||
self.runner = mp.Process(target=envrun,
|
||||
args=(actor, env, self.episode_num, config, self.runcondition),
|
||||
kwargs = {'iterations': config['replay_skip'] + 1,
|
||||
'memoryqueue' : self.memory_queue, 'logqueue' : self.logqueue, 'name' : name})
|
||||
self.runner.start()
|
||||
|
||||
def run(self):
|
||||
self.runcondition.set()
|
||||
|
||||
def join(self):
|
||||
self._sync_memory()
|
||||
if self.logwriter is not None:
|
||||
self.logwriter.write(self._get_reward_logger())
|
||||
|
||||
def _sync_memory(self):
|
||||
if self.memory is not None:
|
||||
for i in range(self.config['replay_skip'] + 1):
|
||||
self.memory.append(*self.memory_queue.get())
|
||||
|
||||
def _get_reward_logger(self):
|
||||
return self.logqueue.get()
|
||||
|
||||
def terminate(self):
|
||||
self.runner.terminate()
|
||||
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ class Network:
|
|||
|
||||
def __call__(self, *args):
|
||||
return self.model(*args)
|
||||
|
||||
|
||||
def clamp_gradients(self, x = 1):
|
||||
assert x > 0
|
||||
for param in self.model.parameters():
|
||||
|
|
|
|||
Loading…
Reference in a new issue