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PPOFP_old.py
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PPOFP_old.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
from dataset import Dataset
class PPO(object):
""" if current policy is p=[p1, p2], then B(p)=[br(p2), br(p1)]"""
def __init__(self, flags, player):
self.flags = flags
self.player = player # 0 or 1
self.opponent_policy = None
self.policy = None # explicit policy
self.value = None # explicit value
np.random.seed(flags.seed)
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=flags.num_cpu,
intra_op_parallelism_threads=flags.num_cpu)
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph, config=tf_config)
with self.graph.as_default():
tf.set_random_seed(flags.seed)
self.env = LeducRLEnv(card_num=flags.card_num, seed=flags.seed)
# build inference
# policy and v
self.state_history = tf.placeholder(tf.int32, [None] + self.env.state_history_space, 'state_history')
self.state_card = tf.placeholder(tf.int32, [None] + self.env.state_card_space, 'state_card')
state_history = tf.reshape(tf.cast(self.state_history, tf.float32), [-1] + [reduce(lambda x, y: x * y, self.env.state_history_space)])
state_card = tf.cast(self.state_card, tf.float32)
self.state = tf.concat([state_card, state_history], axis=1)
with tf.variable_scope('current'):
self.pi_logits, self.v = self._policy_v()
self.pi_softmax = tf.nn.softmax(self.pi_logits) # for test
with tf.variable_scope('old'):
self.pi_logits_old, self.v_old = self._policy_v()
# build training
self.lr = tf.placeholder(tf.float32, [], 'learning_rate')
self.global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
self.action = tf.placeholder(tf.int32, [None])
self.advantage = tf.placeholder(tf.float32, [None])
self.ret = tf.placeholder(tf.float32, [None]) # empirical return
one_hot_actions = tf.one_hot(self.action, self.env.action_space, dtype=tf.float32)
self.neglog_pi = tf.nn.softmax_cross_entropy_with_logits(logits=self.pi_logits, labels=one_hot_actions)
self.log_pi = -self.neglog_pi
self.neglog_pi_old = tf.nn.softmax_cross_entropy_with_logits(logits=self.pi_logits_old, labels=one_hot_actions)
self.log_pi_old = -self.neglog_pi_old
# entropy
a0 = self.pi_logits - tf.reduce_max(self.pi_logits, axis=-1, keep_dims=True)
ea0 = tf.exp(a0)
z0 = tf.reduce_sum(ea0, axis=-1, keep_dims=True)
p0 = ea0 / z0
self.pi_entropy = tf.reduce_sum(p0 * (tf.log(z0) - a0), axis=-1)
self.mean_pi_entropy = tf.reduce_mean(self.pi_entropy)
# kl(pi_new, pi_old)
a1= self.pi_logits_old - tf.reduce_max(self.pi_logits_old, axis=-1, keep_dims=True)
ea1 = tf.exp(a1)
z1 = tf.reduce_sum(ea1, axis=-1, keep_dims=True)
self.kl = tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), axis=-1)
self.mean_kl = tf.reduce_mean(self.kl)
ratio = tf.exp(self.log_pi - tf.stop_gradient(self.log_pi_old)) # pi_new / pi_old
surr1 = ratio * self.advantage
surr2 = tf.clip_by_value(ratio, 1.0 - flags.ppo_clip, 1.0 + flags.ppo_clip) * self.advantage
self.surr_loss = -tf.reduce_mean(tf.minimum(surr1, surr2)) # maximize surrogate objective
# self.surr_loss = -tf.reduce_mean(surr1) # maximize surrogate objective
self.vf_loss = tf.reduce_mean(tf.square(self.v - self.ret)) # minimize value loss
self.entropy_loss = - flags.encoeff * tf.reduce_mean(self.pi_entropy) # maximize entropy
total_loss = self.surr_loss + self.vf_loss + self.entropy_loss
# self.optimizer = tf.train.AdamOptimizer(self.lr)
self.optimizer = tf.train.GradientDescentOptimizer(self.lr)
grad_var_list = self.optimizer.compute_gradients(total_loss)
self.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].name.find('linear1') != -1:
self.train_grad_var_list.append((grad_var_list[i][0]/2, grad_var_list[i][1]))
else:
self.train_grad_var_list.append(grad_var_list[i])
self.apply_gradients = self.optimizer.apply_gradients(self.train_grad_var_list, self.global_step)
with tf.name_scope('copy4old'):
assign_ops = []
for (cur, old) in zip(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='current'),
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='old')):
assert cur.name[cur.name.rfind('/') + 1:] == old.name[old.name.rfind('/') + 1:]
assign_ops.append(tf.assign(old, cur))
self.copy_cur2old_op = tf.group(*assign_ops)
self.init = tf.global_variables_initializer()
self.sess.run(self.init)
self.sess.run(self.copy_cur2old_op)
self.data_states_card = np.zeros([self.flags.data_len] + self.env.state_card_space, np.int32)
self.data_states_history = np.zeros([self.flags.data_len] + self.env.state_history_space, np.int32)
self.data_actions = np.zeros([self.flags.data_len], np.int32)
self.data_returns = np.zeros([self.flags.data_len], np.float32)
self.data_advant = np.zeros([self.flags.data_len], np.float32)
self.top = 0
self.episode_start_index = 0
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):
with tf.variable_scope('linear1'):
dim = self.state.get_shape().as_list()[1]; hiddens = 48
linear1 = self._linear_layer(self.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 = self.state.get_shape().as_list()[1]; hiddens = 48
linear1 = self._linear_layer(self.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 compute_self_policy(self, verbose=False):
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:
if self.player == 1 and not verbose: continue
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]
else:
if self.player == 0 and not verbose: continue
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:]
nn_pi = self.sess.run([self.pi_softmax, self.v],
feed_dict={self.state_card: [state_card],
self.state_history: [state_history]})
policy[card + history] = nn_pi[0][0]
value[card + history] = nn_pi[1][0]
self.policy = policy
self.value = value
return policy, value
def learn(self, policy_gradient_train_num, num_games, opponent_policy):
# record the best
best_behaved_policy = None
best_behaved_policy_payoff = -10000
self.opponent_policy = opponent_policy
self.compute_self_policy()
learning_rate = self.flags.lr
last_pg_payoff = -10000.0
for _ in range(policy_gradient_train_num):
self.collect_samples(num_games)
print "player {:d} iter {:d} : training from {:d} games, totally {:d} transitions, lr={:.7f}".format(
self.player, _, num_games, self.top, learning_rate)
self.sess.run(self.copy_cur2old_op)
d = Dataset({'card': self.data_states_card,
'history': self.data_states_history,
'action': self.data_actions,
'return': self.data_returns,
'advantage': self.data_advant}, self.top)
for epoch in range(self.flags.epochs):
print " {: ^13}|{: ^13}|{: ^13}|{: ^13}|{: ^13}".format("meanKL", "meanEntropy", "surr_loss", "vfloss", "entro_loss")
print_times = 0
for batch in d.iterate_once(self.flags.batch):
train_res = self.sess.run([self.mean_kl,
self.mean_pi_entropy,
self.surr_loss,
self.vf_loss,
self.entropy_loss,
self.apply_gradients], feed_dict={
self.state_card: batch['card'],
self.state_history: batch['history'],
self.action: batch['action'],
self.ret: batch['return'],
self.advantage: batch['advantage'],
self.lr: learning_rate
})
if print_times % int(self.top/self.flags.batch/2) == 0:
print " {: ^13.4f}|{: ^13.4f}|{: ^13.4f}|{: ^13.4f}|{: ^13.4f}".format(*train_res[:-1])
print_times += 1
self.compute_self_policy()
if self.player == 0:
realization = XFP.compute_realization(self.policy, self.opponent_policy)
else:
realization = XFP.compute_realization(self.opponent_policy, self.policy)
payoff = XFP.compute_payoff_given_realization(realization)
print 'player {:d} iter {:d} achieves payoff [{:.4f} {:.4f}]'.format(
self.player,
_,
*payoff)
if last_pg_payoff > payoff[self.player] or np.isclose(last_pg_payoff, payoff[self.player], atol=0.0001):
learning_rate = learning_rate * 0.6
last_pg_payoff = payoff[self.player]
if payoff[self.player] > best_behaved_policy_payoff:
best_behaved_policy_payoff = payoff[self.player]
best_behaved_policy = copy.deepcopy(self.policy)
return best_behaved_policy, best_behaved_policy_payoff
def collect_samples(self, num_games):
self.top = 0
self.episode_start_index = 0
for game in xrange(num_games):
self.episode_start_index = self.top
ob = self.env.reset()
while ob['turn'] != -1:
state_str = ob['card_str'] + ob['history_str']
if ob['turn'] == self.player:
action = np.random.choice(2, [], p=self.policy[state_str])
value = self.value[state_str]
self.data_states_card[self.top] = ob['card']
self.data_states_history[self.top] = ob['state']
self.data_actions[self.top] = action
self.data_advant[self.top] = value
self.top += 1
assert self.top < self.flags.data_len
else:
action = np.random.choice(2, [], p=self.opponent_policy[state_str])
ob = self.env.act(action)
episode_return = ob['payoff'][self.player]
while self.episode_start_index != self.top:
self.data_returns[self.episode_start_index] = episode_return
self.data_advant[self.episode_start_index] = episode_return - self.data_advant[self.episode_start_index]
self.episode_start_index += 1