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We propose a method for tackling catastrophic forgetting in deepreinforcement learning that is \textit{agnostic} to the timescale of changes inthe distribution of experiences, does not require knowledge of task boundaries,and can adapt in \textit{continuously} changing environments. In our\textit{policy consolidation} model, the policy network interacts with acascade of hidden networks that simultaneously remember the agent's policy at arange of timescales and regularise the current policy by its own history,thereby improving its ability to learn without forgetting. We find that themodel improves continual learning relative to baselines on a number ofcontinuous control tasks in single-task, alternating two-task, and multi-agentcompetitive self-play settings.

Policy Consolidation for Continual Reinforcement Learning