- Low: It annoys or frustrates me for a moment.
I’m now trying to train a PPO agent in custom env, the step() will cost about 3min.
algo = PPOConfig() algo = algo.environment(env =CustomEnv) algo = algo.framework('torch').build().train()
I notice that in result of train there has num_env_steps_sampled
... num_agent_steps_sampled: 4000 num_agent_steps_trained: 4000 num_env_steps_sampled: 4000 num_env_steps_trained: 4000 num_env_steps_sampled_this_iter: 4000 num_env_steps_trained_this_iter: 4000 timesteps_total: 4000 num_steps_trained_this_iter: 4000 ...
Is there a way to set num_env_steps_sampled?(for PPO and other built-in algorithms)
What’s the best practice of training in expensive step() env?