Off-Policy Behavior

In reinforcement learning, the behavior policy is the policy used to select actions and gather experience from the environment, while the target policy is the policy being evaluated or learned. Off-policy learning allows these policies to be different, enabling an agent to learn from previously collected data or experiences generated by another agent or strategy.

Off-policy learning allows reinforcement learning systems to reuse existing experiences instead of collecting new data for every learning step. This can improve training efficiency and make it possible to learn from demonstrations, historical interactions, or data generated by different policies.