Hello,
when passing a custom tuner configuration to find optimal hyperparameter for an RL problem, I get the following errer:
WARNING algorithm_config.py:2534 – Setting
exploration_config={}
because you set_enable_rl_module_api=True
. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify theforward_exploration
method of the RLModule at hand. On configs that have a default exploration config, this must be done withconfig.exploration_config={}
.
I define a standard configuration like:
ppo_config = (
PPOConfig()
.environment(
env=ENVIRONMENT,
env_config={
"max_episode_steps": MAX_EPISODE_STEPS,
"sampling_time": SAMPLING_TIME,
"render_mode": None,
},
render_env=False,
)
.rollouts(num_rollout_workers=4, num_envs_per_worker=1)
.framework("torch")
.exploration(explore=True)
)
.training(
sgd_minibatch_size=tune.grid_search([8, 16, 32, 64, 128, 256, 512]),
))
and configure the tuner with:
tuner = tune.Tuner(
"PPO",
param_space=ppo_config,
tune_config=tune.TuneConfig(
metric="episode_reward_mean",
mode="max",
num_samples=8,
),
run_config=air.RunConfig(
stop=stop,
checkpoint_config=air.CheckpointConfig(
checkpoint_at_end=True,
checkpoint_frequency=1,
num_to_keep=25,
checkpoint_score_attribute="episode_reward_mean",
checkpoint_score_order="max",
),
),
)
results = tuner.fit()
Can someone privde help, how to fix this issue?
Thanks