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RLE-Foundation / RLeXplore

RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
https://docs.rllte.dev/
MIT License
352 stars 16 forks source link

Training in custom environments #6

Open priyankamandikal opened 1 year ago

priyankamandikal commented 1 year ago

Hi, thanks for the great repository. Is there functionality for testing on custom-user defined envs? If so, how do we do it?

yuanmingqi commented 1 year ago

Hi, of course. You can write an environment following the OpenAI Gym style, and ensure that the observation space and action space are well-defined.