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Minor update for DQN

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Shangtong Zhang
2017-12-21 22:22:36 -07:00
parent 8839614c44
commit 69b7ea53cc
5 changed files with 17 additions and 31 deletions
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main.py
+6 -8
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@@ -14,7 +14,7 @@ def dqn_cart_pole():
config = Config()
config.task_fn = lambda: CartPole()
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, 0.001)
config.network_fn = lambda optimizer_fn: FCNet([8, 50, 200, 2], optimizer_fn)
config.network_fn = lambda: FCNet([8, 50, 200, 2])
# config.network_fn = lambda optimizer_fn: DuelingFCNet([8, 50, 200, 2], optimizer_fn)
config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=10000, min_epsilon=0.1)
config.replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
@@ -75,7 +75,7 @@ def dqn_pixel_atari(name):
config.task_fn = lambda: PixelAtari(name, no_op=30, frame_skip=4, normalized_state=False)
action_dim = config.task_fn().action_dim
config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=0.00025, alpha=0.95, eps=0.01)
config.network_fn = lambda optimizer_fn: NatureConvNet(config.history_length, action_dim, optimizer_fn)
config.network_fn = lambda: NatureConvNet(config.history_length, action_dim)
# config.network_fn = lambda optimizer_fn: DuelingNatureConvNet(config.history_length, n_actions, optimizer_fn)
config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=1000000, min_epsilon=0.1)
config.replay_fn = lambda: Replay(memory_size=1000000, batch_size=32, dtype=np.uint8)
@@ -141,8 +141,7 @@ def dqn_fruit():
config.optimizer_fn = lambda params: torch.optim.SGD(params, 0.01, momentum=0.9)
config.reward_weight = np.ones(10) / 10
config.hybrid_reward = False
config.network_fn = lambda optimizer_fn: FruitHRFCNet(
98, 4, config.reward_weight, optimizer_fn)
config.network_fn = lambda: FruitHRFCNet(98, 4, config.reward_weight)
config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=10000, min_epsilon=0.1)
config.replay_fn = lambda: Replay(memory_size=10000, batch_size=15)
config.discount = 0.95
@@ -163,8 +162,7 @@ def hrdqn_fruit():
config.hybrid_reward = True
config.reward_weight = np.ones(10) / 10
config.optimizer_fn = lambda params: torch.optim.SGD(params, 0.01, momentum=0.9)
config.network_fn = lambda optimizer_fn: FruitHRFCNet(
98, 4, config.reward_weight, optimizer_fn)
config.network_fn = lambda optimizer_fn: FruitHRFCNet(98, 4, config.reward_weight)
config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=10000, min_epsilon=0.1)
config.replay_fn = lambda: HybridRewardReplay(memory_size=10000, batch_size=15)
config.discount = 0.95
@@ -280,7 +278,7 @@ if __name__ == '__main__':
# logger.setLevel(logging.DEBUG)
logger.setLevel(logging.INFO)
dqn_cart_pole()
# dqn_cart_pole()
# async_cart_pole()
# a3c_cart_pole()
# a3c_continuous()
@@ -290,7 +288,7 @@ if __name__ == '__main__':
# dqn_fruit()
# hrdqn_fruit()
# dqn_pixel_atari('PongNoFrameskip-v4')
dqn_pixel_atari('PongNoFrameskip-v4')
# async_pixel_atari('PongNoFrameskip-v4')
# a3c_pixel_atari('PongNoFrameskip-v4')