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REINFORCE: Monte Carlo Policy Gradient solution to Cartpole-v0 with a hidden layer.
# REINFORCE: Monte Carlo Policy Gradient Implementation
# Learn more from Reinforcement Learning: An Introduction (p271)
# by Sutton & Barto
import tensorflow as tf
import gym
import numpy as np
from gym import wrappers
# GLOBAL SETTINGS
RNG_SEED = 8
ENVIRONMENT = "CartPole-v0"
# ENVIRONMENT = "CartPole-v1"
MAX_EPISODES = 1000
HIDDEN_LAYER = True
HIDDEN_SIZE = 6
DISPLAY_WEIGHTS = False # Help debug weight update
RENDER = False # Render the generation representative
gamma = 0.99 # Discount per step
alpha = 0.02205 # Learning rate
# Upload to OpenAI
UPLOAD = False
EPISODE_INTERVAL = 50 # Generate a video at this interval
SESSION_FOLDER = "/tmp/CartPole-experiment-1"
API_KEY = ""
SUCCESS_THRESHOLD = 195
# SUCCESS_THRESHOLD = 475
CONSECUTIVE_TARGET = 100
def record_interval(n):
global EPISODE_INTERVAL
return n % EPISODE_INTERVAL == 0
env = gym.make(ENVIRONMENT)
if UPLOAD:
env = wrappers.Monitor(env, SESSION_FOLDER, video_callable=record_interval)
env.seed(RNG_SEED)
np.random.seed(RNG_SEED)
tf.set_random_seed(RNG_SEED)
input_size = env.observation_space.shape[0]
try:
output_size = env.action_space.shape[0]
except AttributeError:
output_size = env.action_space.n
# Tensorflow network setup
x = tf.placeholder(tf.float32, shape=(None, input_size))
y = tf.placeholder(tf.float32, shape=(None, 1))
expected_returns = tf.placeholder(tf.float32, shape=(None, 1))
w_init = tf.contrib.layers.xavier_initializer()
if HIDDEN_LAYER:
hidden_W = tf.get_variable("W1", shape=[input_size, HIDDEN_SIZE],
initializer=w_init)
hidden_B = tf.Variable(tf.zeros(HIDDEN_SIZE))
dist_W = tf.get_variable("W2", shape=[HIDDEN_SIZE, output_size],
initializer=w_init)
dist_B = tf.Variable(tf.zeros(output_size))
hidden = tf.nn.elu(tf.matmul(x, hidden_W) + hidden_B)
dist = tf.tanh(tf.matmul(hidden, dist_W) + dist_B)
else:
dist_W = tf.get_variable("W1", shape=[input_size, output_size],
initializer=w_init)
dist_B = tf.Variable(tf.zeros(output_size))
dist = tf.tanh(tf.matmul(x, dist_W) + dist_B)
dist_soft = tf.nn.log_softmax(dist)
dist_in = tf.matmul(dist_soft, tf.Variable([[1.], [0.]]))
pi = tf.contrib.distributions.Bernoulli(dist_in)
pi_sample = pi.sample()
log_pi = pi.log_prob(y)
optimizer = tf.train.RMSPropOptimizer(alpha)
train = optimizer.minimize(-1.0 * expected_returns * log_pi)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
def run_episode(environment, render=False):
raw_reward = 0
discounted_reward = 0
cumulative_reward = []
discount = 1.0
states = []
actions = []
obs = environment.reset()
done = False
while not done:
states.append(obs)
cumulative_reward.append(discounted_reward)
if render:
obs.render()
action = sess.run(pi_sample, feed_dict={x: [obs]})[0]
actions.append(action)
obs, reward, done, info = env.step(action[0])
raw_reward += reward
if reward > 0:
discounted_reward += reward * discount
else:
discounted_reward += reward
discount *= gamma
return raw_reward, discounted_reward, cumulative_reward, states, actions
def display_weights(session):
global HIDDEN_LAYER
if HIDDEN_LAYER:
w1 = session.run(hidden_W)
b1 = session.run(hidden_B)
w2 = session.run(dist_W)
b2 = session.run(dist_B)
print(w1, b1, w2, b2)
else:
w1 = session.run(dist_W)
b1 = session.run(dist_B)
print(w1, b1)
returns = []
for ep in range(MAX_EPISODES):
raw_G, discounted_G, cumulative_G, ep_states, ep_actions = \
run_episode(env, RENDER and not UPLOAD)
expected_R = np.transpose([discounted_G - np.array(cumulative_G)])
sess.run(train, feed_dict={x: ep_states, y: ep_actions,
expected_returns: expected_R})
if DISPLAY_WEIGHTS:
display_weights(sess)
returns.append(raw_G)
returns = returns[-CONSECUTIVE_TARGET:]
mean_returns = np.mean(returns)
msg = "Episode: {}, Return: {}, Last {} returns mean: {}"
msg = msg.format(ep, raw_G, CONSECUTIVE_TARGET, mean_returns)
print(msg)
env.close()
if UPLOAD:
gym.upload(SESSION_FOLDER, api_key=API_KEY)
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