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sac.py
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sac.py
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import tensorflow as tf
import numpy as np
import core as cr
from buffer import replay_buffer
from tensorboardX import SummaryWriter
from ou_noise import OU_noise
import gym
class SAC:
def __init__(self):
self.sess = tf.Session()
self.state_size = 33
self.output_size = 4
self.tau = 0.995
self.gamma = 0.99
self.hidden = [400, 300]
self.batch_size = 64
self.pi_lr = 1e-3
self.q_lr = 1e-3
self.action_limit = 1.0
self.memory = replay_buffer(1e5)
self.target_noise = 0.2
self.noise_clip = 0.1
self.alpha = 1e-5
self.num_worker = 20
self.noise = OU_noise(self.output_size, self.num_worker)
self.x_ph, self.a_ph, self.x2_ph, self.r_ph, self.d_ph = \
cr.placeholders(self.state_size, self.output_size, self.state_size, None, None)
with tf.variable_scope('main'):
self.mu, self.pi, self.logp_pi, self.q1, self.q2, self.q1_pi, self.q2_pi, self.v = \
cr.sac_mlp_actor_critic(
x=self.x_ph,
a=self.a_ph,
hidden=self.hidden,
activation=tf.nn.relu,
output_activation=tf.tanh,
output_size=self.output_size,
action_limit=self.action_limit
)
with tf.variable_scope('target'):
_, _, _, _, _, _, _, self.v_targ = \
cr.sac_mlp_actor_critic(
x=self.x2_ph,
a=self.a_ph,
hidden=self.hidden,
activation=tf.nn.relu,
output_activation=tf.tanh,
output_size=self.output_size,
action_limit=self.action_limit
)
self.pi_params = cr.get_vars('main/pi')
self.value_params = cr.get_vars('main/q') + cr.get_vars('main/v')
self.min_q_pi = tf.minimum(self.q1_pi, self.q2_pi)
self.q_backup = tf.stop_gradient(self.r_ph + self.gamma * (1 - self.d_ph) * self.v_targ)
self.v_backup = tf.stop_gradient(self.min_q_pi - self.alpha * self.logp_pi)
self.pi_loss = tf.reduce_mean(self.alpha * self.logp_pi - self.q1_pi)
self.q1_loss = 0.5 * tf.reduce_mean((self.q_backup - self.q1) ** 2)
self.q2_loss = 0.5 * tf.reduce_mean((self.q_backup - self.q2) ** 2)
self.v_loss = 0.5 * tf.reduce_mean((self.v_backup - self.v) ** 2)
self.value_loss = self.q1_loss + self.q2_loss + self.v_loss
self.pi_optimizer = tf.train.AdamOptimizer(self.pi_lr)
self.train_pi_op = self.pi_optimizer.minimize(self.pi_loss, var_list=self.pi_params)
self.value_optimizer = tf.train.AdamOptimizer(self.q_lr)
with tf.control_dependencies([self.train_pi_op]):
self.train_value_op = self.value_optimizer.minimize(self.value_loss, var_list=self.value_params)
with tf.control_dependencies([self.train_value_op]):
self.target_update = tf.group([tf.assign(v_targ, self.tau*v_targ + (1-self.tau)*v_main)
for v_main, v_targ in zip(cr.get_vars('main'), cr.get_vars('target'))])
self.step_ops = [self.pi_loss, self.q1_loss, self.q2_loss, self.v_loss, self.q1, self.q2,
self.v, self.logp_pi, self.train_pi_op, self.train_value_op, self.target_update]
self.target_init = tf.group([tf.assign(v_targ, v_main)
for v_main, v_targ in zip(cr.get_vars('main'), cr.get_vars('target'))])
self.sess.run(tf.global_variables_initializer())
self.sess.run(self.target_init)
def update(self):
data = self.memory.get_sample(sample_size=self.batch_size)
feed_dict = {
self.x_ph : data['state'],
self.x2_ph : data['next_state'],
self.a_ph : data['action'],
self.r_ph : data['reward'],
self.d_ph : data['done']
}
self.sess.run(self.step_ops, feed_dict=feed_dict)
def get_action(self, state, deterministic=False):
act_op = self.mu if deterministic else self.pi
return self.sess.run(act_op, feed_dict={self.x_ph: [state]})[0]
def test(self):
env = gym.make('Pendulum-v0')
while True:
state = env.reset()
done = False
while not done:
env.render()
action = self.get_action(state, 0)
state, _, done,_ = env.step(action)
def run(self):
from mlagents.envs import UnityEnvironment
writer = SummaryWriter('runs/sac')
num_worker = self.num_worker
state_size = self.state_size
output_size = self.output_size
ep = 0
train_size = 5
env = UnityEnvironment(file_name='env/training', worker_id=1)
default_brain = env.brain_names[0]
brain = env.brains[default_brain]
initial_observation = env.reset()
step = 0
start_steps = 100000
states = np.zeros([num_worker, state_size])
for i in range(start_steps):
actions = np.clip(np.random.randn(num_worker, output_size), -self.action_limit, self.action_limit)
actions += self.noise.sample()
env_info = env.step(actions)[default_brain]
next_states = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
for s, ns, r, d, a in zip(states, next_states, rewards, dones, actions):
self.memory.append(s, ns, r, d, a)
states = next_states
if dones[0]:
self.noise.reset()
if i % train_size == 0:
if len(self.memory.memory) > self.batch_size:
self.update()
print('data storing :', float(i / start_steps))
while True:
ep += 1
states = np.zeros([num_worker, state_size])
terminal = False
score = 0
while not terminal:
step += 1
'''
if step > start_steps:
actions = [self.get_action(s) for s in states]
action_random = 'False'
else:
actions = np.clip(np.random.randn(num_worker, output_size), -self.action_limit, self.action_limit)
action_random = 'True'
'''
actions = [self.get_action(s) for s in states]
action_random = 'False'
env_info = env.step(actions)[default_brain]
next_states = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
terminal = dones[0]
for s, ns, r, d, a in zip(states, next_states, rewards, dones, actions):
self.memory.append(s, ns, r, d, a)
score += sum(rewards)
states = next_states
if len(self.memory.memory) > self.batch_size:
if step % train_size == 0:
self.update()
if ep < 1000:
print('step : ', step, '| start steps : ', start_steps, '| episode :', ep, '| score : ', score, '| memory size', len(self.memory.memory), '| action random : ', action_random)
writer.add_scalar('data/reward', score, ep)
writer.add_scalar('data/memory_size', len(self.memory.memory), ep)
if __name__ == '__main__':
agent = SAC()
agent.run()