Hi
How can I do feature importance after i train a DQN.agent in Rllib?
Thank you.
Hey @sherko_salehpour,
You can add FeatureImportance
similar to any other off_policy_estimation_method
to your config and run the algorithm with that config. Something like the following:
from ray.rllib.algorithms.dqn import DQNConfig
from ray.rllib.offline.feature_importance import FeatureImportance
config = (
DQNConfig()
.framework("torch")
.evaluation(
evaluation_interval=1,
off_policy_estimation_methods={
"feature_importance": {
"type": FeatureImportance, "limit_fraction": 1e-3,
}
)
)
Hello
thanks for your response
After building the model, how can I plot the feature importance?
After each round of training you will get a result dict back which will have feature_importance across each dimension of observation computed. You can plot them at any iteration you want.
Please tell me how I can callback feature_importance_score
(With script)
Thank you