Action Masking with RLlib

Parametric actions to improve reinforcement learning

RL algorithms learn via trial and error. The agent searches the state space early on and takes random actions to learn what leads to a good reward. Pretty straightforward.

Unfortunately, this isn’t terribly efficient, especially if we already know something about what makes a good vs. bad action in some states. Thankfully, we can use action masking — a simple technique that sets the probability of bad actions to 0 — to speed learning and improve our policies.