RLlib Utilities#

Here is a list of all the utilities available in RLlib.

Exploration API#

Exploration is crucial in RL for enabling a learning agent to find new, potentially high-reward states by reaching unexplored areas of the environment.

RLlib has several built-in exploration components that the different algorithms use. You can also customize an algorithm’s exploration behavior by sub-classing the Exploration base class and implementing your own logic:

Built-in Exploration components#

Random([x])

Random number generator base class used by bound module functions.

Inference#

Callback hooks#

Setting and getting states#

Scheduler API#

Use a scheduler to set scheduled values for variables (in Python, PyTorch, or TensorFlow) based on an (int64) timestep input. The computed values are usually float32 types.

Built-in Scheduler components#

Methods#

Training Operations Utilities#

Framework Utilities#

Import utilities#

Tensorflow utilities#

Torch utilities#

Numpy utilities#