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Refactor interfaces

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Shangtong Zhang
2017-05-05 22:24:51 -06:00
parent 13212ca2cb
commit de6d52d86f
5 changed files with 16 additions and 85 deletions
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task.py
+9 -12
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@@ -7,6 +7,7 @@
import gym
import sys
from dqn_agent import *
import torch.optim
class BasicTask:
def transfer_state(self, state):
@@ -32,8 +33,8 @@ class MountainCar(BasicTask):
def __init__(self):
self.env = gym.make(self.name)
self.env._max_episode_steps = sys.maxsize
self.network_fn = lambda learning_rate=0.01: FullyConnectedNet([self.state_space_size, 50, 200, self.action_space_size], learning_rate)
self.optimizer_fn = lambda params: torch.optim.SGD(params, 0.001)
self.network_fn = lambda optimizer_fn: FullyConnectedNet([self.state_space_size, 50, 200, self.action_space_size], optimizer_fn)
self.policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.95, min_epsilon=0.1)
self.replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
@@ -48,19 +49,15 @@ class CartPole(BasicTask):
def __init__(self):
self.env = gym.make(self.name)
self.network_fn = lambda learning_rate=0.01: FullyConnectedNet([self.state_space_size, 50, 200, self.action_space_size], learning_rate)
self.policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.95, min_epsilon=0.1)
self.optimizer_fn = lambda params: torch.optim.SGD(params, 0.001)
self.network_fn = lambda optimizer_fn: FullyConnectedNet([self.state_space_size, 50, 200, self.action_space_size], optimizer_fn)
self.policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.99, min_epsilon=0.01)
self.replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
if __name__ == '__main__':
task = MountainCar()
bp_network_fn = lambda learning_rate=0.001: FullyConnectedNet([task.state_space_size, 50, 200, task.action_space_size], learning_rate, gpu=False)
def smd_network_fn(learning_rate=0.001):
bp_network = bp_network_fn(learning_rate)
return SMDNetworkWrapper(bp_network)
agent = DQNAgent(task, smd_network_fn, task.policy_fn, task.replay_fn,
task = CartPole()
optimizer_fn = lambda params: torch.optim.SGD(params, 0.001)
agent = DQNAgent(task, task.network_fn, optimizer_fn, task.policy_fn, task.replay_fn,
task.discount, task.step_limit, task.target_network_update_freq)
window_size = 100
ep = 0