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model.py
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model.py
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#!/usr/bin/python3
# Copyright (c) 2019, SCALE Lab, Brown University
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import tensorflow as tf
import numpy as np
import datetime
import time
from .scl_session import SCLSession as SCLGame
from .fpga_session import FPGASession as FPGAGame
def log(message):
print('[DRiLLS {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now()) + "] " + message)
class Normalizer():
def __init__(self, num_inputs):
self.num_inputs = num_inputs
self.n = tf.zeros(num_inputs)
self.mean = tf.zeros(num_inputs)
self.mean_diff = tf.zeros(num_inputs)
self.var = tf.zeros(num_inputs)
def observe(self, x):
self.n += 1.
last_mean = tf.identity(self.mean)
self.mean += (x-self.mean)/self.n
self.mean_diff += (x-last_mean)*(x-self.mean)
self.var = tf.clip_by_value(self.mean_diff/self.n, clip_value_min=1e-2, clip_value_max=1000000000)
def normalize(self, inputs):
obs_std = tf.sqrt(self.var)
return (inputs - self.mean)/obs_std
def reset(self):
self.n = tf.zeros(self.num_inputs)
self.mean = tf.zeros(self.num_inputs)
self.mean_diff = tf.zeros(self.num_inputs)
self.var = tf.zeros(self.num_inputs)
class A2C:
def __init__(self, options, load_model=False, fpga_mapping=False):
if fpga_mapping:
self.game = FPGAGame(options)
else:
self.game = SCLGame(options)
self.num_actions = self.game.action_space_length
self.state_size = self.game.observation_space_size
self.normalizer = Normalizer(self.state_size)
self.state_input = tf.placeholder(tf.float32, [None, self.state_size])
# Define any additional placeholders needed for training your agent here:
self.actions = tf.placeholder(tf.float32, [None, self.num_actions])
self.discounted_episode_rewards_ = tf.placeholder(tf.float32, [None, ])
self.state_value = self.critic()
self.actor_probs = self.actor()
self.loss_val = self.loss()
self.train_op = self.optimizer()
self.session = tf.Session()
# model saving/restoring
self.model_dir = options['model_dir']
self.saver = tf.train.Saver()
if load_model:
self.saver.restore(self.session, self.model_dir)
log("Model restored.")
else:
self.session.run(tf.global_variables_initializer())
self.gamma = 0.99
self.learning_rate = 0.01
def optimizer(self):
"""
:return: Optimizer for your loss function
"""
return tf.train.AdamOptimizer(0.01).minimize(self.loss_val)
def critic(self):
"""
Calculates the estimated value for every state in self.state_input. The critic should not depend on
any other tensors besides self.state_input.
:return: A tensor of shape [num_states] representing the estimated value of each state in the trajectory.
"""
c_fc1 = tf.contrib.layers.fully_connected(inputs=self.state_input,
num_outputs=10,
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer())
c_fc2 = tf.contrib.layers.fully_connected(inputs=c_fc1,
num_outputs=1,
activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer())
return c_fc2
def actor(self):
"""
Calculates the action probabilities for every state in self.state_input. The actor should not depend on
any other tensors besides self.state_input.
:return: A tensor of shape [num_states, num_actions] representing the probability distribution
over actions that is generated by your actor.
"""
a_fc1 = tf.contrib.layers.fully_connected(inputs=self.state_input,
num_outputs=20,
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer())
a_fc2 = tf.contrib.layers.fully_connected(inputs=a_fc1,
num_outputs=20,
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer())
a_fc3 = tf.contrib.layers.fully_connected(inputs=a_fc2,
num_outputs=self.num_actions,
activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer())
return tf.nn.softmax(a_fc3)
def loss(self):
"""
:return: A scalar tensor representing the combined actor and critic loss.
"""
# critic loss
advantage = self.discounted_episode_rewards_ - self.state_value
critic_loss = tf.reduce_sum(tf.square(advantage))
# actor loss
neg_log_prob = tf.nn.softmax_cross_entropy_with_logits_v2(logits=tf.log(self.actor_probs),
labels=self.actions)
actor_loss = tf.reduce_sum(neg_log_prob * advantage)
neg_log_prob = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.actor_probs,
labels=self.actions)
policy_gradient_loss = tf.reduce_mean(neg_log_prob * self.discounted_episode_rewards_)
# return policy_gradient_loss
return critic_loss + actor_loss
def save_model(self):
save_path = self.saver.save(self.session, self.model_dir)
log("Model saved in path: %s" % str(save_path))
def train_episode(self):
"""
train_episode will be called several times by the drills.py to train the agent. In this method,
we run the agent for a single episode, then use that data to train the agent.
"""
state = self.game.reset()
self.normalizer.reset()
self.normalizer.observe(state)
state = self.normalizer.normalize(state).eval(session=self.session)
done = False
episode_states = []
episode_actions = []
episode_rewards = []
while not done:
log(' iteration: ' + str(self.game.iteration))
action_probability_distribution = self.session.run(self.actor_probs, \
feed_dict={self.state_input: state.reshape([1, self.state_size])})
action = np.random.choice(range(action_probability_distribution.shape[1]), \
p=action_probability_distribution.ravel())
new_state, reward, done, _ = self.game.step(action)
# append this step
episode_states.append(state)
action_ = np.zeros(self.num_actions)
action_[action] = 1
episode_actions.append(action_)
episode_rewards.append(reward)
state = new_state
self.normalizer.observe(state)
state = self.normalizer.normalize(state).eval(session=self.session)
# Now that we have run the episode, we use this data to train the agent
start = time.time()
discounted_episode_rewards = self.discount_and_normalize_rewards(episode_rewards)
_ = self.session.run(self.train_op, feed_dict={self.state_input: np.array(episode_states), \
self.actions: np.array(episode_actions), \
self.discounted_episode_rewards_: discounted_episode_rewards})
end = time.time()
log('Episode Agent Training Time ~ ' + str((start - end) / 60) + ' minutes.')
self.save_model()
return np.sum(episode_rewards)
def discount_and_normalize_rewards(self, episode_rewards):
"""
used internally to calculate the discounted episode rewards
"""
discounted_episode_rewards = np.zeros_like(episode_rewards)
cumulative = 0.0
for i in reversed(range(len(episode_rewards))):
cumulative = cumulative * self.gamma + episode_rewards[i]
discounted_episode_rewards[i] = cumulative
mean = np.mean(discounted_episode_rewards)
std = np.std(discounted_episode_rewards)
discounted_episode_rewards = (discounted_episode_rewards - mean) / std
return discounted_episode_rewards