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Major refactor to support LSTM layer

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Shangtong Zhang
2017-06-08 18:36:04 -06:00
parent af17bbbf94
commit 9c73abfbff
8 changed files with 370 additions and 204 deletions
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main.py
+36 -29
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@@ -6,8 +6,8 @@ def dqn_cart_pole():
config = dict()
config['task_fn'] = lambda: CartPole()
config['optimizer_fn'] = lambda params: torch.optim.RMSprop(params, 0.001)
# config['network_fn'] = lambda optimizer_fn: FullyConnectedNet([8, 50, 200, 2], optimizer_fn)
config['network_fn'] = lambda optimizer_fn: DuelingFullyConnectedNet([8, 50, 200, 2], optimizer_fn)
config['network_fn'] = lambda optimizer_fn: FullyConnectedNet([8, 50, 200, 2], optimizer_fn)
# config['network_fn'] = lambda optimizer_fn: DuelingFullyConnectedNet([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)
config['discount'] = 0.99
@@ -27,16 +27,16 @@ def async_cart_pole():
config = dict()
config['task_fn'] = lambda: CartPole()
config['optimizer_fn'] = lambda params: torch.optim.Adam(params, 0.001)
config['network_fn'] = lambda: FullyConnectedNet([4, 50, 200, 2], gpu=False)
config['policy_fn'] = lambda: GreedyPolicy(epsilon=1.0, final_step=5000, min_epsilon=0.1)
config['bootstrap_fn'] = OneStepQLearning
# config['bootstrap_fn'] = NStepQLearning
# config['bootstrap_fn'] = OneStepSarsa
config['network_fn'] = lambda: FullyConnectedNet([4, 50, 200, 2])
config['policy_fn'] = lambda: GreedyPolicy(epsilon=0.5, final_step=5000, min_epsilon=0.1)
config['bootstrap'] = OneStepQLearning
# config['bootstrap'] = NStepQLearning
# config['bootstrap'] = OneStepSarsa
config['discount'] = 0.99
config['target_network_update_freq'] = 200
config['step_limit'] = 0
config['n_workers'] = 16
config['batch_size'] = 6
config['update_interval'] = 6
config['test_interval'] = 4000
config['test_repetitions'] = 50
config['history_length'] = 1
@@ -45,19 +45,20 @@ def async_cart_pole():
agent.run()
def a3c_cart_pole():
update_interval = 6
config = dict()
config['task_fn'] = lambda: CartPole()
config['optimizer_fn'] = lambda params: torch.optim.Adam(params, 0.001)
config['network_fn'] = lambda: FCActorCriticNet([4, 200, 2], gpu=False)
config['network_fn'] = lambda: FCActorCriticNet([4, 200, 2])
config['policy_fn'] = SamplePolicy
config['bootstrap_fn'] = AdvantageActorCritic
config['bootstrap'] = AdvantageActorCritic
config['discount'] = 0.99
config['target_network_update_freq'] = 0
config['target_network_update_freq'] = 200
config['step_limit'] = 0
config['n_workers'] = 16
config['batch_size'] = 6
config['test_interval'] = 4000
config['update_interval'] = update_interval
config['history_length'] = 1
config['test_interval'] = 4000
config['test_repetitions'] = 50
config['logger'] = gym.logger
agent = AsyncAgent(**config)
@@ -89,23 +90,25 @@ def dqn_pixel_atari(name):
def async_pixel_atari(name):
config = dict()
history_length = 4
history_length = 1
n_actions = 6
config['task_fn'] = lambda: PixelAtari(name, no_op=30, frame_skip=4)
config['task_fn'] = lambda: PixelAtari(name, no_op=30, frame_skip=4, frame_size=42)
config['optimizer_fn'] = lambda params: torch.optim.Adam(params, lr=0.0001)
config['network_fn'] = lambda: NipsConvNet(history_length, n_actions, gpu=False)
config['network_fn'] = lambda: OpenAIConvNet(history_length,
n_actions,
LSTM=False)
config['policy_fn'] = lambda: StochasticGreedyPolicy(epsilons=[1.0, 1.0, 1.0],
final_step=1000000,
min_epsilons=[0.1, 0.01, 0.5],
probs=[0.4, 0.3, 0.3])
# config['bootstrap_fn'] = OneStepQLearning
# config['bootstrap_fn'] = NStepQLearning
config['bootstrap_fn'] = OneStepSarsa
# config['bootstrap'] = OneStepQLearning
config['bootstrap'] = NStepQLearning
# config['bootstrap'] = OneStepSarsa
config['discount'] = 0.99
config['target_network_update_freq'] = 10000
config['step_limit'] = 10000
config['n_workers'] = 16
config['batch_size'] = 32
config['update_interval'] = 32
config['test_interval'] = 50000
config['test_repetitions'] = 1
config['history_length'] = history_length
@@ -117,16 +120,18 @@ def a3c_pixel_atari(name):
config = dict()
history_length = 1
n_actions = 6
config['task_fn'] = lambda: PixelAtari(name, no_op=30, frame_skip=4)
config['task_fn'] = lambda: PixelAtari(name, no_op=30, frame_skip=4, frame_size=42)
config['optimizer_fn'] = lambda params: torch.optim.Adam(params, lr=0.0001)
config['network_fn'] = lambda: ConvActorCriticNet(history_length, n_actions, gpu=False)
config['network_fn'] = lambda: OpenAIConvActorCriticNet(history_length,
n_actions,
LSTM=True)
config['policy_fn'] = SamplePolicy
config['bootstrap_fn'] = AdvantageActorCritic
config['bootstrap'] = AdvantageActorCritic
config['discount'] = 0.99
config['target_network_update_freq'] = 0
config['step_limit'] = 10000
config['n_workers'] = 16
config['batch_size'] = 20
config['update_interval'] = 20
config['test_interval'] = 50000
config['test_repetitions'] = 1
config['history_length'] = history_length
@@ -138,12 +143,14 @@ if __name__ == '__main__':
# gym.logger.setLevel(logging.DEBUG)
gym.logger.setLevel(logging.INFO)
# async_cart_pole()
# dqn_cart_pole()
# dqn_pixel_atari('BreakoutNoFrameskip-v3')
# async_pixel_atari('BreakoutNoFrameskip-v3')
# a3c_pixel_atari('BreakoutNoFrameskip-v3')
# async_cart_pole()
# a3c_cart_pole()
# dqn_pixel_atari('PongNoFrameskip-v3')
async_pixel_atari('PongNoFrameskip-v3')
# async_pixel_atari('PongNoFrameskip-v3')
# a3c_pixel_atari('PongNoFrameskip-v3')
# dqn_pixel_atari('BreakoutNoFrameskip-v3')
async_pixel_atari('BreakoutNoFrameskip-v3')
# a3c_pixel_atari('BreakoutNoFrameskip-v3')