Restoring RLlib Run Using Tuner.restore

[Finebouche]

Hi, this is a mystery for many people in this forum.
Here are some example of past conversations:

How to resume training from a checkpoint

How severe does this issue affect your experience of using Ray? High: It blocks me to complete my task. Hi everyone. Using ray.tune, I am able to train my model and saving the states as training goes. However, I still haven’t found a way to reuse my trained policy on another setting. (by reusing the neural network in a different experiment). What is the official way to do this ?

Fails restoring weights #41508

I am publishing here as well as a copie of #41508 as I am not sure where my problem belong. The code of examples/restore_1_of_n_agents_from_checkpoint.py seems to not be working (at least in my case). The weight are not recovered but re-initialized. The way I see it is that instead of having the same policy reward means (in Wandb) as before I get reinitialized values. Maybe the example is not up to date or maybe I am doing something wrong here. I am using tune.Tuner().fit() and not tune.trai…

Another tune after restoring a PPO algorithm

if i want to continue training my algorithm after saving it in the checkpoint, how can i make it? my idea is show as following but the grammer is not right that algo can not be directly used in the tune.Tuner: algo = config.build() algo.restore(user_checkpoint_dir) results = tune.Tuner( algo, param_space=config, run_config=air.RunConfig( stop=stop, verbose=2, checkpoint_config=air.CheckpointConfig(checkpoint_at_end=True), ), ).fit()