Online Reinforcement Learning in Non-Stationary Context-Driven Environments

TL;DR

This paper introduces LCPO, a novel online reinforcement learning method designed to address catastrophic forgetting in non-stationary environments by anchoring policies on past experiences, leading to improved performance over existing methods.

Contribution

The paper proposes LCPO, a new online RL algorithm that constrains policy updates using past experiences to mitigate catastrophic forgetting without requiring task labels.

Findings

LCPO outperforms baseline methods in non-stationary environments.

LCPO achieves results comparable to offline-trained agents across various contexts.

LCPO effectively maintains performance despite environment dynamics.

Abstract

We study online reinforcement learning (RL) in non-stationary environments, where a time-varying exogenous context process affects the environment dynamics. Online RL is challenging in such environments due to "catastrophic forgetting" (CF). The agent tends to forget prior knowledge as it trains on new experiences. Prior approaches to mitigate this issue assume task labels (which are often not available in practice), employ brittle regularization heuristics, or use off-policy methods that suffer from instability and poor performance. We present Locally Constrained Policy Optimization (LCPO), an online RL approach that combats CF by anchoring policy outputs on old experiences while optimizing the return on current experiences. To perform this anchoring, LCPO locally constrains policy optimization using samples from experiences that lie outside of the current context distribution. We evaluate LCPO in Mujoco, classic control and computer systems environments with a variety of synthetic and real context traces, and find that it outperforms a variety of baselines in the non-stationary setting, while achieving results on-par with a "prescient" agent trained offline across all context traces. LCPO's source code is available at https://github.com/pouyahmdn/LCPO.