mirror of
https://github.com/wassname/DeepRL.git
synced 2026-09-17 11:50:51 +08:00
Code cleanup
This commit is contained in:
Shangtong Zhang
@@ -20,7 +20,6 @@ def dqn_cart_pole():
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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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@@ -41,7 +40,6 @@ def async_cart_pole():
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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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@@ -156,54 +154,33 @@ def a3c_pixel_atari(name):
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agent = AsyncAgent(config)
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agent.run()
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def dqn_fruit():
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def a2c_pixel_atari(name):
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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: FruitHRFCNet(98, 4, config.reward_weight)
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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.num_workers = 16
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task_fn = lambda: PixelAtari(name, no_op=30, frame_skip=4, frame_size=42, max_steps=10000)
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config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers)
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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.task.env.action_space.n, LSTM=False, gpu=True)
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config.reward_shift_fn = lambda r: np.sign(r)
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config.policy_fn = SamplePolicy
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config.discount = 0.99
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config.gae_tau = 0.97
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config.entropy_weight = 0.01
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config.rollout_length = 20
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config.test_interval = 1000
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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(98, 4, config.reward_weight)
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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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run_episodes(A2CAgent(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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# config.task_fn = lambda: Box2DContinuous('BipedalWalker-v2')
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# config.task_fn = lambda: Box2DContinuous('BipedalWalkerHardcore-v2')
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# config.task_fn = lambda: Box2DContinuous('LunarLanderContinuous-v2')
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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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@@ -214,7 +191,6 @@ def a3c_continuous():
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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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@@ -228,8 +204,9 @@ def a3c_continuous():
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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: Box2DContinuous('BipedalWalker-v2')
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# config.task_fn = lambda: Box2DContinuous('BipedalWalkerHardcore-v2')
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# config.task_fn = lambda: Box2DContinuous('LunarLanderContinuous-v2')
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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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@@ -248,7 +225,6 @@ def p3o_continuous():
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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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@@ -261,10 +237,11 @@ def p3o_continuous():
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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: Box2DContinuous('BipedalWalker-v2')
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# config.task_fn = lambda: Box2DContinuous('BipedalWalkerHardcore-v2')
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# config.task_fn = lambda: Box2DContinuous('LunarLanderContinuous-v2')
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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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@@ -277,7 +254,6 @@ def d3pg_continuous():
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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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@@ -294,14 +270,15 @@ def d3pg_continuous():
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def ddpg_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: Pendulum()
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# config.task_fn = lambda: Box2DContinuous('BipedalWalker-v2')
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# config.task_fn = lambda: Box2DContinuous('BipedalWalkerHardcore-v2')
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# config.task_fn = lambda: Box2DContinuous('LunarLanderContinuous-v2')
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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: Roboschool('RoboschoolReacher-v1')
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# config.task_fn = lambda: Roboschool('RoboschoolHopper-v1')
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# config.task_fn = lambda: Roboschool('RoboschoolAnt-v1')
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# config.task_fn = lambda: Roboschool('RoboschoolWalker2d-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, 1, non_linear=F.relu, batch_norm=False, gpu=False)
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@@ -313,7 +290,6 @@ def ddpg_continuous():
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lambda params: torch.optim.Adam(params, lr=1e-3, weight_decay=0.01)
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config.replay_fn = lambda: HighDimActionReplay(memory_size=1000000, batch_size=64)
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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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@@ -335,21 +311,19 @@ if __name__ == '__main__':
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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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dqn_cart_pole()
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# async_cart_pole()
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# a3c_cart_pole()
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a2c_cart_pole()
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# a2c_cart_pole()
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# a3c_continuous()
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# p3o_continuous()
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# d3pg_continuous()
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# ddpg_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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# a2c_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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