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@Smerity
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Script for Cartpole using policy gradient via Chainer, two layer MLP, dropout, and rejection sampling of historical memories
''' Script for Cartpole using policy gradient via Chainer, two layer MLP, dropout, and rejection sampling of historical memories '''
import gym
import numpy as np
import chainer
from chainer import optimizers
from chainer import ChainList, Variable
import chainer.functions as F
import chainer.links as L
env = gym.make('CartPole-v0')
env.monitor.start('./cartpole-experiment')
print('Action space:', env.action_space)
print('Observation space:', env.observation_space)
INPUT = 4
HIDDEN = 32
MEMORY_STORE = 16
REWARD_DECAY = 0.99
EPSILON_RANDOM = 0.01
MINIMUM_UPDATE_SIZE = 2
SGD_LR = 0.8
DROPOUT = 0.5
class PolicyNetwork(ChainList):
def __init__(self, input_size=4, hidden_size=32):
super(PolicyNetwork, self).__init__(
L.Linear(input_size, hidden_size, nobias=True),
L.Linear(hidden_size, 1, nobias=True),
)
def __call__(self, x, train=True, dropout=0.5):
h = x
h = F.dropout(self[0](h), train=train, ratio=dropout)
h = self[1](F.tanh(h))
return F.sigmoid(h)
model = PolicyNetwork(input_size=INPUT, hidden_size=HIDDEN)
optimizer = optimizers.SGD(lr=SGD_LR)
optimizer.setup(model)
env.reset()
episodes = []
reward_history = []
for iter in range(10000):
episode = []
total_reward = 0
state = env.reset()
for t in range(201):
env.render()
raw_action = model(np.array([state], dtype=np.float32), train=False)
action = 1 if np.random.random() < raw_action.data else 0
if np.random.random() > 1 - EPSILON_RANDOM:
action = env.action_space.sample()
new_state, reward, done, info = env.step(action)
episode.append((state, action, reward))
state = new_state
total_reward += reward
if done:
break
episodes.append((total_reward, episode))
reward_history.append(total_reward)
if len(episodes) > MINIMUM_UPDATE_SIZE:
gradW = [[], []]
for _, episode in episodes:
R = [r for idx, (s, a, r) in enumerate(episode)]
accR = [sum(r * REWARD_DECAY ** i for i, r in enumerate(R[idx:])) for idx, (s, a, r) in enumerate(episode)]
pred_actions = [model(np.array([s], dtype=np.float32), train=True, dropout=DROPOUT) for (s, a, r) in episode]
losses = [(pa - a) ** 2 for pa, (s, a, r) in zip(pred_actions, episode)]
for loss, r in zip(losses, accR):
model.zerograds()
loss.backward()
gradW[0].append(r * model[0].W.grad)
gradW[1].append(r * model[1].W.grad)
for idx, gradW in enumerate(gradW):
model[idx].W.grad = np.mean(gradW, axis=0, dtype=np.float32)
optimizer.update()
maxR = np.max([r for (r, ep) in episodes])
episodes = [(r, ep) for (r, ep) in episodes if maxR * np.random.random() < r]
np.random.shuffle(episodes)
episodes = episodes[:MEMORY_STORE]
print('Episode {} finished after {} timesteps (avg for last 100 - {})'.format(iter, t, np.mean(reward_history[-100:])))
env.monitor.close()
@Smerity
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Smerity commented Sep 2, 2016

This script fixes a major bug in gradient calculation from the previous version. The policy gradient also uses epsilon random exploration and rejection sampling of episode memories based upon the best seen reward so far to help minimize variance and improve convergence.

Evaluation history:

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