from Displayer import DISPLAYER
from Saver import SAVER
import parameters
if __name__ == '__main__':
tf.reset_default_graph()
with tf.Session() as sess:
agent = Agent(sess)
SAVER.set_sess(sess)
SAVER.load(agent)
print("Beginning of the run")
try:
agent.run()
except KeyboardInterrupt:
pass
print("End of the run")
SAVER.save(agent.total_steps)
DISPLAYER.disp()
# agent.play(10)
# agent.play(3, "results/gif/".format(parameters.ENV))
agent.close()
def run(self):
self.total_steps = 0
for ep in range(1, parameters.TRAINING_STEPS + 1):
episode_reward = 0
episode_step = 0
done = False
# Initial state
s = self.env.reset()
self.env.set_render(ep % 1000 == 0)
gif = (ep % 1500 == 0)
step_allonge = ep // 1000
while episode_step < parameters.MAX_EPISODE_STEPS + step_allonge \
and not done:
if random.random() < self.epsilon:
a = self.env.random()
else:
# choose action based on deterministic policy
a, = self.sess.run(self.network.actions,
feed_dict={self.network.state_ph: [s]})
s_, r, done, info = self.env.act(a, gif)
episode_reward += r
self.buffer.add((s, a, r, s_, 0.0 if done else 1.0))
# update network weights to fit a minibatch of experience
if self.total_steps % parameters.TRAINING_FREQ == 0 and \
len(self.buffer) >= parameters.BATCH_SIZE:
minibatch = self.buffer.sample()
_, _ = self.sess.run([self.network.critic_train_op, self.network.actor_train_op],
feed_dict={
self.network.state_ph: np.asarray([elem[0] for elem in minibatch]),
self.network.action_ph: np.asarray([elem[1] for elem in minibatch]),
self.network.reward_ph: np.asarray([elem[2] for elem in minibatch]),
self.network.next_state_ph: np.asarray([elem[3] for elem in minibatch]),
self.network.is_not_terminal_ph: np.asarray([elem[4] for elem in minibatch])})
# update target networks
_ = self.sess.run(self.network.update_slow_targets_op)
s = s_
episode_step += 1
self.total_steps += 1
# Decay epsilon
if self.epsilon > parameters.EPSILON_STOP:
self.epsilon -= self.epsilon_decay
if gif:
self.env.save_gif('results/gif/', self.n_gif)
self.n_gif = (self.n_gif + 1) % 5
if episode_reward > self.best_run:
self.best_run = episode_reward
print("Save best", episode_reward)
SAVER.save('best')
DISPLAYER.add_reward(episode_reward)
if ep % 50 == 0:
print('Episode %2i, Reward: %7.3f, Steps: %i, Epsilon: %7.3f'
' (max step: %i)' % (ep, episode_reward, episode_step,
self.epsilon,
parameters.MAX_EPISODE_STEPS +
step_allonge))
if ep % 500 == 0:
DISPLAYER.disp()
