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Pixel atari games with DQN

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Shangtong Zhang
2017-05-20 22:09:18 -06:00
parent 7c4919d6ac
commit ad8c8ad853
8 changed files with 151 additions and 50 deletions
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main.py
+28 -19
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@@ -1,12 +1,13 @@
from async_agent import *
from dqn_agent import *
import logging
def async_cart_pole():
config = dict()
config['task_fn'] = lambda: CartPole()
config['optimizer_fn'] = lambda params: torch.optim.SGD(params, 0.001)
config['network_fn'] = lambda: FullyConnectedNet([4, 50, 200, 2])
config['policy_fn'] = lambda: GreedyPolicy(epsilon=1.0, end_episode=500, min_epsilon=0.1)
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
@@ -25,7 +26,7 @@ def async_lunar_lander():
config['task_fn'] = lambda: LunarLander()
config['optimizer_fn'] = lambda params: torch.optim.Adam(params, 0.001)
config['network_fn'] = lambda: FullyConnectedNet([8, 50, 200, 4])
config['policy_fn'] = lambda: GreedyPolicy(epsilon=1.0, end_episode=2000, min_epsilon=0.05)
config['policy_fn'] = lambda: GreedyPolicy(epsilon=1.0, final_step=40000, min_epsilon=0.05)
config['bootstrap_fn'] = OneStepQLearning
config['discount'] = 0.99
config['target_network_update_freq'] = 200
@@ -37,30 +38,19 @@ def async_lunar_lander():
agent = AsyncAgent(**config)
agent.run()
# Mountain Car is fairly unstable
def dqn_mountain_car():
config = dict()
config['task_fn'] = lambda: MountainCar()
config['optimizer_fn'] = lambda params: torch.optim.SGD(params, 0.001)
config['network_fn'] = lambda optimizer_fn: FullyConnectedNet([2, 50, 200, 3], optimizer_fn)
config['policy_fn'] = lambda: GreedyPolicy(epsilon=0.5, end_episode=500, min_epsilon=0.1)
config['replay_fn'] = lambda: Replay(memory_size=10000, batch_size=10)
config['discount'] = 0.99
config['target_network_update_freq'] = 1000
config['step_limit'] = 5000
agent = DQNAgent(**config)
agent.run()
def dqn_cart_pole():
config = dict()
config['task_fn'] = lambda: CartPole()
config['optimizer_fn'] = lambda params: torch.optim.SGD(params, 0.001)
config['network_fn'] = lambda optimizer_fn: FullyConnectedNet([4, 50, 200, 2], optimizer_fn)
config['policy_fn'] = lambda: GreedyPolicy(epsilon=1.0, end_episode=500, min_epsilon=0.1)
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
config['target_network_update_freq'] = 200
config['step_limit'] = 300
config['step_limit'] = 0
config['explore_steps'] = 1000
config['logger'] = gym.logger
config['history_length'] = 1
agent = DQNAgent(**config)
agent.run()
@@ -81,9 +71,28 @@ def actor_critic_cart_pole():
agent = AsyncAgent(**config)
agent.run()
def dqn_pixel_atari(name):
config = dict()
config['task_fn'] = lambda: PixelAtari(name)
config['optimizer_fn'] = lambda params: torch.optim.RMSprop(params, lr=0.00025)
config['network_fn'] = lambda optimizer_fn: ConvNet(4, 6, 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)
config['discount'] = 0.99
config['target_network_update_freq'] = 10000
config['step_limit'] = 0
config['explore_steps'] = 50000
config['logger'] = gym.logger
config['history_length'] = 4
agent = DQNAgent(**config)
agent.run()
if __name__ == '__main__':
# gym.logger.setLevel(logging.DEBUG)
gym.logger.setLevel(logging.INFO)
# async_cart_pole()
# async_lunar_lander()
# dqn_cart_pole()
# dqn_mountain_car()
actor_critic_cart_pole()
# actor_critic_cart_pole()
dqn_pixel_atari('Breakout-v0')