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pg_agent.py
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pg_agent.py
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import random
import time
import os
import logging
import csv
import sys
import tempfile
import numpy as np
import tensorflow as tf
import gym
from gym import envs, scoreboard
from gym.spaces import Discrete, Box
import prettytensor as pt
from value_function import ValueFunction
from space_conversion import SpaceConversionEnv
DTYPE = tf.float32
RENDER_EVERY = None
MONITOR = True
logger = logging.getLogger('pg_agent')
logger.setLevel(logging.INFO)
# Sample from the probability distribution.
def cat_sample(prob_nk):
assert prob_nk.ndim == 2
N = prob_nk.shape[0]
csprob_nk = np.cumsum(prob_nk, axis=1)
out = np.zeros(N, dtype='i')
for (n, csprob_k, r) in zip(xrange(N), csprob_nk, np.random.rand(N)):
for (k, csprob) in enumerate(csprob_k):
if csprob > r:
out[n] = k
break
return out
def write_csv(file_name, *arrays):
with open(file_name, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
for row in zip(*arrays):
writer.writerow(row)
def discount_rewards(r, gamma):
""" take 1D float array of rewards and compute discounted reward """
discounted_r = np.zeros_like(r)
running_add = 0
for t in reversed(xrange(0, r.size)):
#if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)
running_add = running_add * gamma + r[t]
discounted_r[t] = running_add
return discounted_r
# Learning agent. Encapsulates training and prediction.
class PGAgent(object):
def __init__(self, env, H, timesteps_per_batch, learning_rate, gamma, epochs, dropout):
if not isinstance(env.observation_space, Box) or \
not isinstance(env.action_space, Discrete):
logger.error("Incompatible spaces.")
exit(-1)
self.H = H
self.timesteps_per_batch = timesteps_per_batch
self.learning_rate = learning_rate
self.gamma = gamma
self.epochs = epochs
self.dropout = dropout
self.env = env
self.session = tf.Session()
# Full state used for next action prediction. Contains current
# observation, previous observation and previous action.
self.obs = tf.placeholder(
DTYPE,
shape=[None, 2 * env.observation_space.shape[0] + env.action_space.n],
name="obs")
self.prev_obs = np.zeros(env.observation_space.shape[0])
self.prev_action = np.zeros(env.action_space.n)
# One hot encoding of the actual action taken.
self.action = tf.placeholder(DTYPE,
shape=[None, env.action_space.n],
name="action")
# Advantage, obviously.
self.advant = tf.placeholder(DTYPE, shape=[None], name="advant")
# Old policy prediction.
self.prev_policy = tf.placeholder(DTYPE,
shape=[None, env.action_space.n],
name="prev_policy")
self.policy_network, _ = (
pt.wrap(self.obs)
.fully_connected(H, activation_fn=tf.nn.tanh)
.dropout(self.dropout)
.softmax_classifier(env.action_space.n))
self.returns = tf.placeholder(DTYPE,
shape=[None, env.action_space.n],
name="returns")
loss = - tf.reduce_sum(tf.mul(self.action,
tf.div(self.policy_network,
self.prev_policy)), 1) * self.advant
self.train = tf.train.AdamOptimizer().minimize(loss)
features_count = 2 * env.observation_space.shape[0] + env.action_space.n + 2
self.value_function = ValueFunction(self.session,
features_count,
learning_rate=1e-3,
epochs=50,
dropout=0.5)
self.session.run(tf.initialize_all_variables())
def rollout(self, max_pathlength, timesteps_per_batch, render=False):
paths = []
timesteps_sofar = 0
while timesteps_sofar < timesteps_per_batch:
obs, actions, rewards, action_dists, actions_one_hot = [], [], [], [], []
ob = self.env.reset()
self.prev_action *= 0.0
self.prev_obs *= 0.0
for x in xrange(max_pathlength):
if render:
env.render()
obs_new = np.expand_dims(
np.concatenate([ob, self.prev_obs, self.prev_action], 0), 0)
action_dist_n = self.session.run(self.policy_network, {self.obs: obs_new})
action = int(cat_sample(action_dist_n)[0])
self.prev_obs = ob
self.prev_action *= 0.0
self.prev_action[action] = 1.0
obs.append(ob)
actions.append(action)
action_dists.append(action_dist_n)
actions_one_hot.append(np.copy(self.prev_action))
res = list(self.env.step(action))
rewards.append(res[1])
ob = res[0]
if res[2]:
path = {"obs": np.concatenate(np.expand_dims(obs, 0)),
"action_dists": np.concatenate(action_dists),
"rewards": np.array(rewards),
"actions": np.array(actions),
"actions_one_hot": np.array(actions_one_hot)}
paths.append(path)
self.prev_action *= 0.0
self.prev_obs *= 0.0
timesteps_sofar += len(path["rewards"])
break
else:
timesteps_sofar += max_pathlength
return paths
def prepare_features(self, path):
obs = path["obs"]
prev_obs = np.concatenate([np.zeros((1,obs.shape[1])), path["obs"][1:]], 0)
prev_action = path['action_dists']
return np.concatenate([obs, prev_obs, prev_action], axis=1)
def predict(self, path):
features = self.prepare_features(path)
return self.session.run(self.policy_network, {self.obs: features})
def learn(self):
self.current_observation = self.env.reset()
xs,hs,dlogps,drs = [],[],[],[]
running_reward = None
reward_sum = 0
episode_number = 0
current_step = 0.0
iteration_number = 0
discounted_eprs = []
mean_path_lens = []
value_function_losses = []
while True:
render = not RENDER_EVERY is None and 0 == iteration_number % RENDER_EVERY
paths = self.rollout(max_pathlength=10000,
timesteps_per_batch=self.timesteps_per_batch,
render=render)
for path in paths:
path["baseline"] = self.value_function.predict(path)
path["prev_policy"] = self.predict(path)
path["returns"] = discount_rewards(path["rewards"], self.gamma)
path["advant"] = path["returns"] - path["baseline"]
value_function_losses.append(self.value_function.validate(paths))
self.value_function.fit(paths)
features = np.concatenate([self.prepare_features(path) for path in paths])
advant = np.concatenate([path["advant"] for path in paths])
advant -= advant.mean()
advant /= (advant.std() + 1e-8)
actions = np.concatenate([path["actions_one_hot"] for path in paths])
prev_policy = np.concatenate([path["prev_policy"] for path in paths])
for _ in range(self.epochs):
self.session.run(self.train, {self.obs: features,
self.advant: advant,
self.action: actions,
self.prev_policy: prev_policy})
iteration_number += 1
mean_path_len = np.mean([len(path['rewards']) for path in paths])
mean_path_lens.append(mean_path_len)
logger.info("Iteration %s mean_path_len: %s", iteration_number, mean_path_len)
if iteration_number > 100:
paths = self.rollout(max_pathlength=10000, timesteps_per_batch=40000)
ret = np.mean([len(path['rewards']) for path in paths]), np.mean(mean_path_lens)
logger.info("Validation result: %s", ret[0])
if not MONITOR:
write_csv('/tmp/out.csv', mean_path_lens, value_function_losses)
return ret
if __name__ == '__main__':
seed = 1
random.seed(seed)
np.random.seed(seed)
tf.set_random_seed(seed)
env_name = "CartPole-v0" if len(sys.argv) < 2 else sys.argv[1]
env = gym.make(env_name)
env = SpaceConversionEnv(env, Box, Discrete)
if MONITOR:
training_dir = tempfile.mkdtemp()
env.monitor.start(training_dir)
agent = PGAgent(env,
H=109,
timesteps_per_batch=1369,
learning_rate=0.028609296254614544,
gamma=0.9914327475117531,
epochs=4,
dropout=0.5043049954791183)
agent.learn()
if MONITOR:
env.monitor.close()
gym.upload(training_dir, api_key='sk_lgS7sCv1Qxq5HFMdQXR6Sw')