'end_time': first_experiment_end_time
})
# Schedule second experiment
second_experiment_start_time = '185530' # format: %H%M%S e.g. 2:30pm is 143000
second_experiment_end_time = '191000' # format: %H%M%S e.g. 4:00pm is 160000
second_experiment_start_delay = (datetime.strptime(
date.today().strftime("%Y%m%d") + '-' + second_experiment_start_time,
'%Y%m%d-%H%M%S') - open_time).total_seconds()
if second_experiment_start_delay < 0:
logging.error(
'Second Experiment starts earlier than the end of First Experiment!')
scheduler.enter(second_experiment_start_delay,
1,
interact_with_learning_agent,
kwargs={
'agent': LAS_agent_community,
'env': envLAS,
'end_time': second_experiment_end_time
})
if __name__ == '__main__':
# Run two experiments
logging.info('Run scheduler...')
scheduler.run()
logging.info('Scheduler done!')
envLAS.destroy()
reward = 0
done = False
start_time = time.time()
try:
for i in range(max_episodes):
observation = env.reset()
ep_reward = 0
for j in range(max_episode_len):
if render_env == True:
env.render()
# Added exploration noise
action = LASAgent.perceive_and_act(observation, reward,
done)
observation, reward, done, info = env.step(action[0])
ep_reward += reward
if done or j == (max_episode_len - 1):
print('| Reward: {:d} | Episode: {:d} '.format(
int(ep_reward), i))
episod_reward_memory.append(ep_reward)
plot_cumulative_reward(episod_reward_memory)
break
#time.sleep(0.5)
print("Time elapsed:{}".format(time.time() - start_time))
except KeyboardInterrupt:
sess.close()
env.destroy()
print("Shut Down.")