mirror of
https://github.com/wassname/DeepRL.git
synced 2026-08-22 11:40:47 +08:00
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
from async_agent import *
|
|
from dqn_agent import *
|
|
|
|
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['bootstrap_fn'] = OneStepQLearning
|
|
# config['bootstrap_fn'] = NStepQLearning
|
|
config['bootstrap_fn'] = OneStepSarsa
|
|
config['discount'] = 0.99
|
|
config['target_network_update_freq'] = 200
|
|
config['step_limit'] = 300
|
|
config['n_workers'] = 8
|
|
config['batch_size'] = 5
|
|
config['test_interval'] = 500
|
|
config['test_repeats'] = 5
|
|
agent = AsyncAgent(**config)
|
|
agent.run()
|
|
|
|
def async_lunar_lander():
|
|
config = dict()
|
|
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['bootstrap_fn'] = OneStepQLearning
|
|
config['discount'] = 0.99
|
|
config['target_network_update_freq'] = 200
|
|
config['step_limit'] = 5000
|
|
config['n_workers'] = 8
|
|
config['batch_size'] = 10
|
|
config['test_interval'] = 1000
|
|
config['test_repeats'] = 5
|
|
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['replay_fn'] = lambda: Replay(memory_size=10000, batch_size=10)
|
|
config['discount'] = 0.99
|
|
config['target_network_update_freq'] = 200
|
|
config['step_limit'] = 300
|
|
agent = DQNAgent(**config)
|
|
agent.run()
|
|
|
|
if __name__ == '__main__':
|
|
async_cart_pole()
|
|
# async_lunar_lander()
|
|
# dqn_cart_pole()
|
|
# dqn_mountain_car()
|