mirror of
https://github.com/wassname/DeepRL.git
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297 lines
12 KiB
Python
297 lines
12 KiB
Python
import logging
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from agent import *
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from component import *
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from utils import *
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import model.action_conditional_video_prediction as acvp
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def dqn_cart_pole():
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config = Config()
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config.task_fn = lambda: CartPole()
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config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
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config.network_fn = lambda optimizer_fn: FCNet([8, 50, 200, 2], optimizer_fn)
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# config.network_fn = lambda optimizer_fn: DuelingFCNet([8, 50, 200, 2], optimizer_fn)
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config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=10000, min_epsilon=0.1)
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config.replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
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config.discount = 0.99
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config.target_network_update_freq = 200
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config.max_episode_length = 200
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config.exploration_steps = 1000
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config.logger = Logger('./log', logger)
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config.history_length = 2
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config.test_interval = 100
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config.test_repetitions = 50
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config.double_q = True
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# config.double_q = False
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run_episodes(DQNAgent(config))
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def async_cart_pole():
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config = Config()
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config.task_fn= lambda: CartPole()
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config.optimizer_fn = lambda params: torch.optim.Adam(params, 0.001)
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config.network_fn = lambda: FCNet([4, 50, 200, 2])
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config.policy_fn = lambda: GreedyPolicy(epsilon=0.5, final_step=5000, min_epsilon=0.1)
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# config.worker = OneStepQLearning
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config.worker = NStepQLearning
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# config.worker = OneStepSarsa
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config.discount = 0.99
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config.target_network_update_freq = 200
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config.max_episode_length = 200
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config.num_workers = 16
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config.update_interval = 6
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config.test_interval = 1
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config.test_repetitions = 50
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config.logger = Logger('./log', logger)
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agent = AsyncAgent(config)
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agent.run()
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def a3c_cart_pole():
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config = Config()
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config.task_fn = lambda: CartPole()
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config.optimizer_fn = lambda params: torch.optim.Adam(params, 0.001)
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config.network_fn = lambda: ActorCriticFCNet(4, 2)
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config.policy_fn = SamplePolicy
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config.worker = AdvantageActorCritic
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config.discount = 0.99
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config.max_episode_length = 200
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config.num_workers = 16
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config.update_interval = 6
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config.test_interval = 1
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config.test_repetitions = 30
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config.logger = Logger('./log', logger)
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config.gae_tau = 1.0
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config.entropy_weight = 0.01
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agent = AsyncAgent(config)
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agent.run()
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def dqn_pixel_atari(name):
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config = Config()
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config.history_length = 4
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config.task_fn = lambda: PixelAtari(name, no_op=30, frame_skip=4, normalized_state=False)
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action_dim = config.task_fn().action_dim
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config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=0.00025, alpha=0.95, eps=0.01)
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config.network_fn = lambda optimizer_fn: NatureConvNet(config.history_length, action_dim, optimizer_fn)
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# config.network_fn = lambda optimizer_fn: DuelingNatureConvNet(config.history_length, n_actions, optimizer_fn)
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config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=1000000, min_epsilon=0.1)
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config.replay_fn = lambda: Replay(memory_size=1000000, batch_size=32, dtype=np.uint8)
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config.discount = 0.99
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config.target_network_update_freq = 10000
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config.max_episode_length = 0
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config.exploration_steps= 50000
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config.logger = Logger('./log', logger)
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config.test_interval = 10
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config.test_repetitions = 1
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# config.double_q = True
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config.double_q = False
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run_episodes(DQNAgent(config))
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def async_pixel_atari(name):
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config = Config()
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config.history_length = 1
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config.task_fn = lambda: PixelAtari(name, no_op=30, frame_skip=4, frame_size=42)
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task = config.task_fn()
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config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=0.0001)
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config.network_fn = lambda: OpenAIConvNet(
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config.history_length, task.env.action_space.n)
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config.policy_fn = lambda: StochasticGreedyPolicy(
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epsilons=[0.7, 0.7, 0.7], final_step=2000000, min_epsilons=[0.1, 0.01, 0.5],
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probs=[0.4, 0.3, 0.3])
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# config.worker = OneStepSarsa
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# config.worker = NStepQLearning
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config.worker = OneStepQLearning
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config.discount = 0.99
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config.target_network_update_freq = 10000
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config.max_episode_length = 10000
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config.num_workers = 6
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config.update_interval = 20
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config.test_interval = 50000
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config.test_repetitions = 1
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config.logger = Logger('./log', logger)
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agent = AsyncAgent(config)
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agent.run()
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def a3c_pixel_atari(name):
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config = Config()
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config.history_length = 1
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config.task_fn = lambda: PixelAtari(name, no_op=30, frame_skip=4, frame_size=42)
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task = config.task_fn()
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config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=0.0001)
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config.network_fn = lambda: OpenAIActorCriticConvNet(
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config.history_length, task.env.action_space.n, LSTM=True)
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config.policy_fn = SamplePolicy
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config.worker = AdvantageActorCritic
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config.discount = 0.99
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config.max_episode_length = 10000
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config.num_workers = 6
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config.update_interval = 20
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config.test_interval = 50000
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config.test_repetitions = 1
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config.logger = Logger('./log', logger)
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agent = AsyncAgent(config)
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agent.run()
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def dqn_fruit():
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config = Config()
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config.task_fn = lambda: Fruit()
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config.optimizer_fn = lambda params: torch.optim.SGD(params, 0.01, momentum=0.9)
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config.reward_weight = np.ones(10) / 10
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config.hybrid_reward = False
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config.network_fn = lambda optimizer_fn: FruitHRFCNet(
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98, 4, config.reward_weight, optimizer_fn)
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config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=10000, min_epsilon=0.1)
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config.replay_fn = lambda: Replay(memory_size=10000, batch_size=15)
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config.discount = 0.95
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config.target_network_update_freq = 200
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config.max_episode_length = 100
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config.exploration_steps = 200
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config.logger = Logger('./log', logger)
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config.history_length = 1
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config.test_interval = 0
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config.test_repetitions = 10
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config.episode_limit = 5000
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config.double_q = False
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run_episodes(DQNAgent(config))
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def hrdqn_fruit():
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config = Config()
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config.task_fn = lambda: Fruit(hybrid_reward=True)
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config.hybrid_reward = True
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config.reward_weight = np.ones(10) / 10
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config.optimizer_fn = lambda params: torch.optim.SGD(params, 0.01, momentum=0.9)
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config.network_fn = lambda optimizer_fn: FruitHRFCNet(
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98, 4, config.reward_weight, optimizer_fn)
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config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=10000, min_epsilon=0.1)
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config.replay_fn = lambda: HybridRewardReplay(memory_size=10000, batch_size=15)
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config.discount = 0.95
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config.target_network_update_freq = 200
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config.max_episode_length = 100
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config.exploration_steps = 200
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config.logger = Logger('./log', logger)
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config.history_length = 1
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config.test_interval = 0
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config.test_repetitions = 10
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config.target_type = config.expected_sarsa_target
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# config.target_type = config.q_target
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config.double_q = False
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config.episode_limit = 5000
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run_episodes(DQNAgent(config))
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def a3c_continuous():
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config = Config()
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config.task_fn = lambda: Pendulum()
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# config.task_fn = lambda: BipedalWalkerHardcore()
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task = config.task_fn()
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config.actor_optimizer_fn = lambda params: torch.optim.Adam(params, 0.0001)
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config.critic_optimizer_fn = lambda params: torch.optim.Adam(params, 0.001)
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config.network_fn = lambda: DisjointActorCriticNet(
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# lambda: GaussianActorNet(task.state_dim, task.action_dim, unit_std=False, action_gate=F.tanh, action_scale=2.0),
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lambda: GaussianActorNet(task.state_dim, task.action_dim, unit_std=True),
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lambda: GaussianCriticNet(task.state_dim))
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config.policy_fn = lambda: GaussianPolicy()
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config.worker = ContinuousAdvantageActorCritic
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config.discount = 0.99
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config.max_episode_length = task.max_episode_steps
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config.num_workers = 8
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config.update_interval = 20
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config.test_interval = 1
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config.test_repetitions = 1
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config.entropy_weight = 0
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config.gradient_clip = 40
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config.logger = Logger('./log', logger)
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agent = AsyncAgent(config)
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agent.run()
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def p3o_continuous():
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config = Config()
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config.task_fn = lambda: Pendulum()
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# config.task_fn = lambda: BipedalWalker()
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# config.task_fn = lambda: BipedalWalkerHardcore()
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# config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
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# config.task_fn = lambda: Roboschool('RoboschoolAnt-v1')
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task = config.task_fn()
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config.actor_network_fn = lambda: GaussianActorNet(task.state_dim, task.action_dim,
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gpu=False, unit_std=True)
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config.critic_network_fn = lambda: GaussianCriticNet(task.state_dim, gpu=False)
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config.network_fn = lambda: DisjointActorCriticNet(config.actor_network_fn, config.critic_network_fn)
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config.actor_optimizer_fn = lambda params: torch.optim.Adam(params, 0.001)
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config.critic_optimizer_fn = lambda params: torch.optim.Adam(params, 0.001)
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config.policy_fn = lambda: GaussianPolicy()
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config.replay_fn = lambda: GeneralReplay(memory_size=2048, batch_size=2048)
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config.worker = ProximalPolicyOptimization
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config.discount = 0.99
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config.gae_tau = 0.97
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config.num_workers = 6
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config.test_interval = 1
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config.test_repetitions = 1
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config.max_episode_length = task.max_episode_steps
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config.entropy_weight = 0
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config.gradient_clip = 20
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config.rollout_length = 10000
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config.optimize_epochs = 1
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config.ppo_ratio_clip = 0.2
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config.logger = Logger('./log', logger)
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agent = AsyncAgent(config)
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agent.run()
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def d3pg_continuous():
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config = Config()
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config.task_fn = lambda: Pendulum()
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# config.task_fn = lambda: ContinuousLunarLander()
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# config.task_fn = lambda: Roboschool('RoboschoolInvertedPendulum-v1')
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# config.task_fn = lambda: Roboschool('RoboschoolReacher-v1')
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# config.task_fn = lambda: BipedalWalker()
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task = config.task_fn()
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config.actor_network_fn = lambda: DeterministicActorNet(
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task.state_dim, task.action_dim, F.tanh, 2, non_linear=F.relu, batch_norm=False)
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config.critic_network_fn = lambda: DeterministicCriticNet(
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task.state_dim, task.action_dim, non_linear=F.relu, batch_norm=False)
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config.network_fn = lambda: DisjointActorCriticNet(config.actor_network_fn, config.critic_network_fn)
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config.actor_optimizer_fn = lambda params: torch.optim.Adam(params, lr=1e-4)
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config.critic_optimizer_fn =\
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lambda params: torch.optim.Adam(params, lr=1e-4)
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config.replay_fn = lambda: SharedReplay(memory_size=1000000, batch_size=64,
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state_shape=(task.state_dim, ), action_shape=(task.action_dim, ))
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config.discount = 0.99
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config.max_episode_length = task.max_episode_steps
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config.random_process_fn = \
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lambda: OrnsteinUhlenbeckProcess(size=task.action_dim, theta=0.15, sigma=0.2,
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n_steps_annealing=100000)
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config.worker = DeterministicPolicyGradient
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config.num_workers = 6
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config.min_memory_size = 50
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config.target_network_mix = 0.001
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config.test_interval = 500
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config.test_repetitions = 1
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config.gradient_clip = 20
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config.logger = Logger('./log', logger)
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agent = AsyncAgent(config)
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agent.run()
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if __name__ == '__main__':
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mkdir('data')
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mkdir('log')
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os.system('export OMP_NUM_THREADS=1')
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# logger.setLevel(logging.DEBUG)
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logger.setLevel(logging.INFO)
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# dqn_cart_pole()
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# async_cart_pole()
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# a3c_cart_pole()
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# a3c_continuous()
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# p3o_continuous()
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# d3pg_continuous()
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# dqn_fruit()
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# hrdqn_fruit()
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# dqn_pixel_atari('PongNoFrameskip-v4')
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# async_pixel_atari('PongNoFrameskip-v4')
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# a3c_pixel_atari('PongNoFrameskip-v4')
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# dqn_pixel_atari('BreakoutNoFrameskip-v4')
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# async_pixel_atari('BreakoutNoFrameskip-v4')
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# a3c_pixel_atari('BreakoutNoFrameskip-v4')
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acvp.train('PongNoFrameskip-v4')
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