我们可以看到在lr=0.0001,vf_share_layers=False的时候,episode_reward_mean是最高的。 《讯飞创意组机器人导航原理与仿真指南》课程重点讲解ROS移动机器人导航原理与实现方法,带领大家解析讯飞创意组线上赛的赛事规则、熟悉并上手比赛使用...
print("episode mean reward: ", result["episode_reward_mean"] ) # 3. train it, 输出结果为: 该教程的所有代码将放在仓库: https://github.com/OpenRL-Lab/Ray_Tutorial/ 本次教程仅对Ray的用法进行概述,其他例如Ray Core、Ray RLlib、Ray AIR的更多详细用法,将在以后的教程中进行介绍,相关代码也将...
print('episode total=',results['episodes_total']) print('timesteps total=',results['timesteps_total']) print('episode_reward_mean=',results['episode_reward_mean']) if results['episode_reward_mean']>=199: break print('='*20) print('episode total=',results['episodes_total']) print('t...
运行该命令后,RLlib会创建具有命名的项目并记录重要的指标,如reward或episode_reward_mean。在训练运行的输出中,你还应该看到有关机器(loc,表示主机名和端口)以及训练运行状态的信息。如果运行状态为TERMINATED,但在日志中未看到成功运行的项目,则可能出现了问题。下面是训练运行的示例输出: 当成功完成训练时,可以看到如...
best_trainer = max(trainers, key=lambda trainer: trainer.evaluate()['episode_reward_mean'])print(best_trainer.evaluate())3.2 在大数据领域的影响高效处理大规模数据:大数据时代,Ray 的分布式计算能力处理大规模数据集,为大数据分析提供工具。如电商企业利用 Ray 并行处理用户交易数据,发现购买行为模式和潜在需求...
'episode_reward_mean': 500 } st=time.time() results = tune.run( 'PPO', # Specify the algorithm to train config=config, stop=stop ) print('elapsed time=',time.time()-st) ray.shutdown() 运行代码后,在浏览器中访问http://127.0.0.1:8265/,类似tensorboard的使用,就可以观察训练过程中的资源...
avg_reward_finished = np.mean(self.rewards) self.memory.clear() def train_policy(self): # episode结束,训练策略网络 n_batches = int(self.T / self.batch_size) + 1 # 计算Generalized Advantage Estimation self.opt.zero_grad() obs_ctxs, acts, act_logprobs, returns, advantages = self....
I can obtain episode reward mean from the train result, but the fluctuation is very large, and it is difficult to judge when to stop the training iteration, so I hope to use the result of evaluate. I tried two methods, but both failed (ray=2.38.0). Method 1 uses evaluation config. ...
... episode_reward_max: 1.0 episode_reward_mean: 1.0 episode_reward_min: 1.0 episodes_this_iter: 15 episodes_total: 19 ... timesteps_total: 10000 training_iteration: 10 ... 特别是,这个输出显示每一集平均达到的最小奖励为 1.0,这意味着代理程序总是能够达到目标并收集到最大奖励(1.0)。 保存...
/.../TrainLogs/" # Metrics self.metrics = 'episode_reward_mean' # Find out how to make sure that self.mode = 'max' # Checkpoints self.score = 'episode_reward_mean' self.checkpoint_frequency = 100 self.num_to_keep =2 # Others self.verbose =1 # Register Model in Ray Registry ...