scores_window.append(score) # save most recent score
list_scores.append(score) # save most recent score
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_window)),
end="")
if i_episode % 100 == 0:
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_window)))
return list_scores
if __name__ == '__main__':
print(opt)
env = UnityEnvironment(file_name="Reacher.app")
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=True)[brain_name]
state_size, action_size = get_settings(env_info, brain)
agent1 = Agent(state_size, action_size, opt.seed)
agent1.load_actor(opt.actor_model_path)
agent1.load_critic(opt.critic_model_path)
agent2 = Agent(state_size, action_size, opt.seed)
agent2.load_actor(opt.actor_model_path)
agent2.load_critic(opt.critic_model_path)
scores = infer_agent(env, agent1, agent2, brain_name)
env.close()
#agent.step(state, action, reward, next_state, done)
state = next_state
score += reward
if done:
break
scores_window.append(score) # save most recent score
scores.append(score) # save most recent score
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_window)),
end="")
if i_episode % 100 == 0:
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_window)))
return scores
if __name__ == '__main__':
print(opt)
env = UnityEnvironment(file_name="Reacher.app")
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=True)[brain_name]
state_size, action_size = get_settings(env_info, brain)
agent = Agent(state_size, action_size, opt.seed)
agent = agent.load_actor(opt.actor_model_path)
agent = agent.load_critic(opt.critic_model_path)
scores = infer_agent(env, agent, brain_name)
env.close()
