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Script for Cartpole using policy gradient via Chainer, two layer MLP, dropout, and rejection sampling of historical memories
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''' 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() |
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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: