I am using
rllib to train a customized gym environment defined by myself.
But the tune process can not start to train since the trial is not ready
In detailed, the
ready list from
ready, _ = ray.wait(shuffled_results, timeout=timeout) in
tune.ray_trial_executor.RayTrialExecutor.get_next_available_trial() does not include my trial_id so trial executor can not access the environment, so training in the environment can not start.
Actually, I tried many times and only few can start to train. In most cases, the
ready list doesn’t include the environment. I also tried changing the
timeout in the function but it didnot solve the problem so I think time is not the matter.
I also tried the common gym environment
CartPole and it is always included in the
ready list so the training can start without problems.
I wonder if you have any ideas about why one environment is not ready by
The most low-level function called is
ready_ids, remaining_ids = worker.core_worker.wait( object_refs, num_returns, timeout_milliseconds, worker.current_task_id, fetch_local, )
Appreciate any help about this problem!