for i in range(NUM_EPISODES):
cur_state = env.reset()
cum_reward = 0
# tensorboard summary
summary_writer = tf.summary.FileWriter('/tmp/pendulum-log-0'+'/train', graph=tf.get_default_graph())
if (i % EVALUATE_EVERY) == 0:
print ('====evaluation====')
for t in range(MAX_STEPS):
if (i % EVALUATE_EVERY) == 0:
env.render()
action = agent.get_action(cur_state, sess)[0]
else:
# decaying noise
action = agent.get_action_noise(cur_state, sess, rate=(NUM_EPISODES-i)/NUM_EPISODES)[0]
next_state, reward, done, info = env.step(action)
if (i % EVALUATE_EVERY) == 0:
print('cur_state: ', end='')
print(cur_state)
print('action: ', end='')
print(action)
print('reward: ' + str(reward))
print('------------------------------------------------------')
if done:
cum_reward += reward
agent.add_step(Step(cur_step=cur_state, action=action, next_step=next_state, reward=reward, done=done))
print("Done! Episode {} finished after {} timesteps, cum_reward: {}".format(i, t + 1, cum_reward))
summarize(cum_reward, i, summary_writer)
break
cum_reward += reward
agent = DDPG(actor=actor, critic=critic, exprep=exprep, noise=noise, action_bound=setting.ACTION_RANGE)
sess.run(tf.initialize_all_variables())
for i in range(setting.NUM_EPISODES):
cur_state = env.reset()
cum_reward = 0
if (i % setting.EVALUATE_EVERY) == 0:
print ('====evaluation====')
for t in range(setting.MAX_STEPS):
print("Time step: " + str(t))
if (i % setting.EVALUATE_EVERY) == 0:
env.render()
action = agent.get_action(cur_state, sess)[0]
else:
# decaying noise
action = agent.get_action_noise(cur_state, sess, rate=(setting.NUM_EPISODES-i)/setting.NUM_EPISODES)[0]
action = convertToPositive(action)
next_state, reward, done, info = env.step(action)
infos = info.split(',')
rew_history.append(reward)
util_history.append(float(infos[2]))
delay_history.append(float(infos[3]))
cum_reward += reward
agent.add_step(Step(cur_step=cur_state, action=action, next_step=next_state, reward=reward, done=done))
if (i % setting.EVALUATE_EVERY) == 0:
printer.print_state(cur_state)
printer.do_job('action', action)
printer.do_job('reward, util, delay', [reward, float(infos[2]), float(infos[3])])
printer.do_line()
if done or t == setting.MAX_STEPS - 1:
print("Done! Episode {} finished after {} timesteps, cum_reward: {}".format(i, t + 1, cum_reward))
