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PPOFP.py
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PPOFP.py
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import numpy as np
import tensorflow as tf
import tensorflow.contrib as tfc
import copy
from XFP import XFP, LeducRLEnv
class PPO(object):
""" if current policy is p=[p1, p2], then B(p)=[br(p2), br(p1)]"""
def __init__(self, flags):
self.flags = flags
self.explicit_policy = None # explicit policy only possible to be evaluated in small games
self.explicit_value = None # explicit value
self.opponent_policy = None
np.random.seed(flags.seed)
self.env = LeducRLEnv(card_num=flags.card_num, seed=flags.seed)
tf_config = tf.ConfigProto(
allow_soft_placement=True,
inter_op_parallelism_threads=flags.num_cpu,
intra_op_parallelism_threads=flags.num_cpu)
self.device = '/gpu:0' if self.flags.use_gpu else '/cpu:0'
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph, config=tf_config)
self.sess.__enter__()
tf.set_random_seed(flags.seed)
with tf.device(self.device):
self.lr = tf.get_variable('learning_rate', [], tf.float32, tf.constant_initializer(self.flags.lr), trainable=False)
self.ops = [{}, {}] # ops for position1 and position2
for i in range(2):
with tf.variable_scope("player"+str(i)):
# build data pipeline
self.ops[i]['state_history'] = tf.placeholder(tf.int32, [None] + self.env.state_history_space, 'state_history')
self.ops[i]['state_card'] = tf.placeholder(tf.int32, [None] + self.env.state_card_space, 'state_card')
self.ops[i]['action'] = tf.placeholder(tf.int32, [None])
self.ops[i]['advantage'] = tf.placeholder(tf.float32, [None])
self.ops[i]['ret'] = tf.placeholder(tf.float32, [None]) # empirical return
dataset = tf.data.Dataset.from_tensor_slices((self.ops[i]['state_history'],
self.ops[i]['state_card'],
self.ops[i]['action'],
self.ops[i]['advantage'],
self.ops[i]['ret']))
dataset = dataset.shuffle(10000)
dataset = dataset.batch(self.flags.batch)
dataset = dataset.repeat(self.flags.epochs)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
self.ops[i]['iterator'] = iterator
self.ops[i]['next_element'] = next_element
# build training
self.ops[i]['pi_logits'], self.ops[i]['v'], self.ops[i]['pi_logits_old'], self.ops[i]['v_old'], _ = \
self._build_inference(next_element[0], next_element[1])
self.ops[i]['mean_kl'], self.ops[i]['mean_pi_entropy'], self.ops[i]['surr_loss'], \
self.ops[i]['vf_loss'], self.ops[i]['apply_gradients'], self.ops[i]['copy'] = \
self._build_train(next_element[2], next_element[3], next_element[4], self.ops[i]['pi_logits'],
self.ops[i]['pi_logits_old'], self.ops[i]['v'])
# build inference
self.ops[i]['state_history_inf'] = tf.placeholder(tf.int32, [None] + self.env.state_history_space,
'state_history_inf')
self.ops[i]['state_card_inf'] = tf.placeholder(tf.int32, [None] + self.env.state_card_space,
'state_card_inf')
_, self.ops[i]['v_inf'], _, _, self.ops[i]['softmax'] = self._build_inference(
self.ops[i]['state_history_inf'], self.ops[i]['state_card_inf'], reuse=True)
self.paramerters_place_holders = []
parameters_op_list = []
for parameter in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, ".*current.*"):
ph = tf.placeholder(tf.float32, shape=parameter.get_shape())
self.paramerters_place_holders.append(ph)
parameters_op_list.append(tf.assign(parameter, ph))
self.load_op = tf.group(*parameters_op_list)
self.init = tf.global_variables_initializer()
self.sess.run(self.init)
# build data pipeline
self.data_states_card = np.zeros([2, self.flags.data_len] + self.env.state_card_space, np.int32)
self.data_states_history = np.zeros([2, self.flags.data_len] + self.env.state_history_space, np.int32)
self.data_actions = np.zeros([2, self.flags.data_len], np.int32)
self.data_returns = np.zeros([2, self.flags.data_len], np.float32)
self.data_advant = np.zeros([2, self.flags.data_len], np.float32)
self.top = [0, 0]
self.episode_start_index = [0, 0]
def _build_inference(self, state_history_ph, state_card_ph, reuse=False):
# policy and v
state_history = tf.reshape(tf.cast(state_history_ph, tf.float32), [-1] + [reduce(lambda x, y: x * y, self.env.state_history_space)])
state_card = tf.cast(state_card_ph, tf.float32)
state = tf.concat([state_card, state_history], axis=1)
with tf.variable_scope('current'):
pi_logits, v = self._policy_v(state, reuse)
pi_softmax = tf.nn.softmax(pi_logits, name="pi_softmax") # for test
with tf.variable_scope('old'):
pi_logits_old, v_old = self._policy_v(state, reuse)
return pi_logits, v, pi_logits_old, v_old, pi_softmax
def _build_train(self, action, advantage, ret, pi_logits, pi_logits_old, v):
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0),
trainable=False)
# build training
# action = tf.placeholder(tf.int32, [None])
# advantage = tf.placeholder(tf.float32, [None])
# ret = tf.placeholder(tf.float32, [None]) # empirical return
one_hot_actions = tf.one_hot(action, self.env.action_space, dtype=tf.float32)
neglog_pi = tf.nn.softmax_cross_entropy_with_logits(logits=pi_logits, labels=one_hot_actions)
log_pi = -neglog_pi
neglog_pi_old = tf.nn.softmax_cross_entropy_with_logits(logits=pi_logits_old, labels=one_hot_actions)
log_pi_old = -neglog_pi_old
# entropy
a0 = pi_logits - tf.reduce_max(pi_logits, axis=-1, keep_dims=True)
ea0 = tf.exp(a0)
z0 = tf.reduce_sum(ea0, axis=-1, keep_dims=True)
p0 = ea0 / z0
pi_entropy = tf.reduce_sum(p0 * (tf.log(z0) - a0), axis=-1)
mean_pi_entropy = tf.reduce_mean(pi_entropy, name="mean_entropy")
# kl(pi_new, pi_old)
a1 = pi_logits_old - tf.reduce_max(pi_logits_old, axis=-1, keep_dims=True)
ea1 = tf.exp(a1)
z1 = tf.reduce_sum(ea1, axis=-1, keep_dims=True)
kl = tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), axis=-1)
mean_kl = tf.reduce_mean(kl, name="mean_kl")
ratio = tf.exp(log_pi - tf.stop_gradient(log_pi_old)) # pi_new / pi_old
surr1 = ratio * advantage
surr2 = tf.clip_by_value(ratio, 1.0 - self.flags.ppo_clip, 1.0 + self.flags.ppo_clip) * advantage
surr_loss = -tf.reduce_mean(tf.minimum(surr1, surr2), name="surrogate_loss") # maximize surrogate objective
vf_loss = tf.reduce_mean(tf.square(v - ret), name="value_loss") # minimize value loss
entropy_loss = - self.flags.encoeff * tf.reduce_mean(pi_entropy) # maximize entropy
# total_loss = surr_loss + vf_loss + entropy_loss
total_loss = surr_loss + vf_loss # not use entropy
# self.optimizer = tf.train.AdamOptimizer(self.lr)
optimizer = tf.train.GradientDescentOptimizer(self.lr)
grad_var_list = optimizer.compute_gradients(total_loss)
train_grad_var_list = []
for i in range(len(grad_var_list)):
if grad_var_list[i][0] is None: continue
if grad_var_list[i][1] in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name + ".*current/linear1"):
train_grad_var_list.append((grad_var_list[i][0]/2, grad_var_list[i][1]))
else:
train_grad_var_list.append(grad_var_list[i])
apply_gradients = optimizer.apply_gradients(train_grad_var_list, global_step)
assign_ops = []
for (cur, old) in zip(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=tf.get_variable_scope().name + '.*current'),
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=tf.get_variable_scope().name + '.*old')):
assign_ops.append(tf.assign(old, cur))
copy_cur2old_op = tf.group(*assign_ops, name="copy")
return mean_kl, mean_pi_entropy, surr_loss, vf_loss, apply_gradients, copy_cur2old_op
def _linear_layer(self, linear_in, dim, hiddens):
weights = tf.get_variable('weights', [dim, hiddens], tf.float32,
initializer=tfc.layers.variance_scaling_initializer(mode='FAN_AVG', uniform=True))
bias = tf.get_variable('bias', [hiddens], tf.float32,
initializer=tf.constant_initializer(0.1))
pre_activations = tf.add(tf.matmul(linear_in, weights), bias)
linear_out = tf.nn.relu(pre_activations)
return linear_out
def _policy_v(self, state, reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope('linear1'):
dim = state.get_shape().as_list()[1]; hiddens = 48
linear1 = self._linear_layer(state, dim, hiddens)
with tf.variable_scope('policy'):
with tf.variable_scope('linear2'):
dim = hiddens; hiddens = 32
linear2 = self._linear_layer(linear1, dim, hiddens)
with tf.variable_scope('linear3'):
dim = hiddens; hiddens = self.env.action_space
pi_logits = self._linear_layer(linear2, dim, hiddens)
with tf.variable_scope('linear1'):
tf.get_variable_scope().reuse_variables()
dim = state.get_shape().as_list()[1]; hiddens = 48
linear1 = self._linear_layer(state, dim, hiddens)
with tf.variable_scope('value'):
with tf.variable_scope('linear2'):
dim = hiddens; hiddens = 32
linear2 = self._linear_layer(linear1, dim, hiddens)
with tf.variable_scope('linear3'):
dim = hiddens; hiddens = 1
v = self._linear_layer(linear2, dim, hiddens)
return pi_logits, v
def get_parameters(self):
return self.sess.run(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, ".*current.*"))
def load_parameters(self, parameters_list):
feed_dict = {}
for i, j in zip(parameters_list, self.paramerters_place_holders):
feed_dict[j] = i
self.sess.run(self.load_op, feed_dict=feed_dict)
def compute_self_policy(self):
policy = [{}, {}]
value = [{}, {}]
for cards in XFP.possible_cards_list:
for history in LeducRLEnv.history_string2vector:
state_card = np.zeros([self.env.card_num], np.int32)
state_history = LeducRLEnv.history_string2vector[history]
pround = 1 if history in XFP.round1_states_set else 2
if history in XFP.player1_states_set:
state_card[int(cards[0])] = 1
card = cards[0]
if pround == 2:
state_card[int(self.env.card_num) / 2 + int(cards[1])] = 1
card = cards[:2]
if card + history in policy[0]: continue
nn_pi_v = self.sess.run([self.ops[0]['softmax'], self.ops[0]['v_inf']],
feed_dict={self.ops[0]['state_card_inf']: [state_card],
self.ops[0]['state_history_inf']: [state_history]})
policy[0][card + history] = nn_pi_v[0][0]
value[0][card + history] = nn_pi_v[1][0]
else:
state_card[int(cards[2])] = 1
card = cards[2]
if pround == 2:
state_card[int(self.env.card_num) / 2 + int(cards[1])] = 1
card = cards[1:]
if card + history in policy[1]: continue
nn_pi_v = self.sess.run([self.ops[1]['softmax'], self.ops[1]['v_inf']],
feed_dict={self.ops[1]['state_card_inf']: [state_card],
self.ops[1]['state_history_inf']: [state_history]})
policy[1][card + history] = nn_pi_v[0][0]
value[1][card + history] = nn_pi_v[1][0]
self.explicit_policy = policy
self.explicit_value = value
return policy, value
def collect_samples(self, num_games, cut2same_size=False):
self.top[0] = self.top[1] = 0
cards_indices = np.random.randint(0, len(XFP.possible_cards_list), num_games)
for collect_for_p in range(2):
for game in xrange(num_games):
self.episode_start_index[collect_for_p] = self.top[collect_for_p]
ob = self.env.reset(XFP.possible_cards_list[cards_indices[game]])
while ob['turn'] != -1:
state_str = ob['card_str'] + ob['history_str']
position = ob['turn']
if position != collect_for_p:
action = np.random.choice(2, [], p=self.opponent_policy[position][state_str])
else:
action = np.random.choice(2, [], p=self.explicit_policy[position][state_str])
value = self.explicit_value[position][state_str]
self.data_states_card[position, self.top[position]] = ob['card']
self.data_states_history[position, self.top[position]] = ob['state']
self.data_actions[position, self.top[position]] = action
self.data_advant[position, self.top[position]] = value
self.top[position] += 1
assert self.top[position] < self.flags.data_len
ob = self.env.act(action)
episode_return = ob['payoff'][collect_for_p]
while self.episode_start_index[collect_for_p] != self.top[collect_for_p]:
self.data_returns[collect_for_p, self.episode_start_index[collect_for_p]] = episode_return
self.data_advant[collect_for_p, self.episode_start_index[collect_for_p]] = \
episode_return - self.data_advant[collect_for_p, self.episode_start_index[collect_for_p]]
self.episode_start_index[collect_for_p] += 1
if cut2same_size:
size = min(self.top)
self.top = [size, size]
def learn(self, policy_gradient_train_num, opponent_policy, num_games=10000):
self.opponent_policy = opponent_policy
for _ in range(policy_gradient_train_num):
self.compute_self_policy()
self.collect_samples(num_games, cut2same_size=True)
print "iter {:d} : training from {:d} games, totally {:d} {:d} transitions".format(
_, num_games, self.top[0], self.top[1])
self.sess.run([self.ops[0]['copy'], self.ops[1]['copy']])
data_pipeline_feed_dict = {}
for i in range(2):
data_pipeline_feed_dict[self.ops[i]['state_history']] = self.data_states_history[i, :self.top[i]]
data_pipeline_feed_dict[self.ops[i]['state_card']] = self.data_states_card[i, :self.top[i]]
data_pipeline_feed_dict[self.ops[i]['action']] = self.data_actions[i, :self.top[i]]
data_pipeline_feed_dict[self.ops[i]['advantage']] = self.data_advant[i, :self.top[i]]
data_pipeline_feed_dict[self.ops[i]['ret']] = self.data_returns[i, :self.top[i]]
self.sess.run([self.ops[0]['iterator'].initializer, self.ops[1]['iterator'].initializer],
feed_dict=data_pipeline_feed_dict)
print " {: ^13}|{: ^13}|{: ^13}|{: ^13}|{: ^13}".format("meanKL", "meanEntropy", "surr_loss", "vfloss", "lr")
print_frequency = 0
while True:
try:
result = self.sess.run([self.ops[0]['mean_kl'],
self.ops[0]['mean_pi_entropy'],
self.ops[0]['surr_loss'],
self.ops[0]['vf_loss'],
self.lr,
self.ops[1]['mean_kl'],
self.ops[1]['mean_pi_entropy'],
self.ops[1]['surr_loss'],
self.ops[1]['vf_loss'],
self.lr,
self.ops[0]['apply_gradients'],
self.ops[1]['apply_gradients']])
if print_frequency % int(self.top[0]/self.flags.batch*self.flags.epochs/4) == 0:
print "p0: {: ^13.4f}|{: ^13.4f}|{: ^13.4f}|{: ^13.4f}|{: ^13.4f}".format(*result[:5])
print "p1: {: ^13.4f}|{: ^13.4f}|{: ^13.4f}|{: ^13.4f}|{: ^13.4f}".format(*result[5:10])
print_frequency += 1
except tf.errors.OutOfRangeError:
break