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Double DQN and Dueling DQN

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Shangtong Zhang
2017-06-03 15:03:17 -06:00
parent 52918587be
commit 353541cd32
5 changed files with 88 additions and 20 deletions
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main.py
+16 -8
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@@ -6,7 +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: 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
@@ -17,6 +18,8 @@ def dqn_cart_pole():
config['history_length'] = 2
config['test_interval'] = 100
config['test_repetitions'] = 50
# config['double_q'] = True
config['double_q'] = False
agent = DQNAgent(**config)
agent.run()
@@ -66,7 +69,8 @@ def dqn_pixel_atari(name):
n_actions = 6
config['task_fn'] = lambda: PixelAtari(name, no_op=30, frame_skip=4, normalized_state=False)
config['optimizer_fn'] = lambda params: torch.optim.RMSprop(params, lr=0.00025, alpha=0.95, eps=0.01)
config['network_fn'] = lambda optimizer_fn: ConvNet(history_length, n_actions, optimizer_fn)
# config['network_fn'] = lambda optimizer_fn: ConvNet(history_length, n_actions, optimizer_fn)
config['network_fn'] = lambda optimizer_fn: DuelingConvNet(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)
config['discount'] = 0.99
@@ -75,9 +79,12 @@ def dqn_pixel_atari(name):
config['explore_steps'] = 50000
config['logger'] = gym.logger
config['history_length'] = history_length
config['test_interval'] = 1000
config['test_repetitions'] = 50
config['test_interval'] = 10
config['test_repetitions'] = 1
# config['double_q'] = True
config['double_q'] = False
agent = DQNAgent(**config)
agent.tag = 'dueling_'
agent.run()
def async_pixel_atari(name):
@@ -88,9 +95,9 @@ def async_pixel_atari(name):
config['optimizer_fn'] = lambda params: torch.optim.Adam(params, lr=0.0001)
config['network_fn'] = lambda: ConvNet(history_length, n_actions, gpu=False)
config['policy_fn'] = lambda: GreedyPolicy(epsilon=1.0, final_step=1000000, min_epsilon=0.1)
config['bootstrap_fn'] = OneStepQLearning
# config['bootstrap_fn'] = OneStepQLearning
# config['bootstrap_fn'] = NStepQLearning
# config['bootstrap_fn'] = OneStepSarsa
config['bootstrap_fn'] = OneStepSarsa
config['discount'] = 0.99
config['target_network_update_freq'] = 10000
config['step_limit'] = 10000
@@ -129,10 +136,11 @@ if __name__ == '__main__':
gym.logger.setLevel(logging.INFO)
# async_cart_pole()
# dqn_cart_pole()
dqn_cart_pole()
# dqn_pixel_atari('BreakoutNoFrameskip-v3')
# async_pixel_atari('BreakoutNoFrameskip-v3')
# a3c_pixel_atari('BreakoutNoFrameskip-v3')
# a3c_cart_pole()
async_pixel_atari('PongNoFrameskip-v3')
# dqn_pixel_atari('PongNoFrameskip-v3')
# async_pixel_atari('PongNoFrameskip-v3')
# a3c_pixel_atari('PongNoFrameskip-v3')