Factored Adaptation for Non-Stationary Reinforcement LearningDownload PDF

Published: 31 Oct 2022, 18:00, Last Modified: 15 Jan 2023, 13:28NeurIPS 2022 AcceptReaders: Everyone
Keywords: non-stationarity, non-stationary RL, causal RL
TL;DR: We introduce a factored adaptation framework for nonstationary RL and show that learned factored representations improve the rewards and robustness under non-stationarity.
Abstract: Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). While most current approaches model the changes as a single shared embedding vector, we leverage insights from the recent causality literature to model non-stationarity in terms of individual latent change factors, and causal graphs across different environments. In particular, we propose Factored Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that learns jointly both the causal structure in terms of a factored MDP, and a factored representation of the individual time-varying change factors. We prove that under standard assumptions, we can completely recover the causal graph representing the factored transition and reward function, as well as a partial structure between the individual change factors and the state components. Through our general framework, we can consider general non-stationary scenarios with different function types and changing frequency, including changes across episodes and within episodes. Experimental results demonstrate that FANS-RL outperforms existing approaches in terms of return, compactness of the latent state representation, and robustness to varying degrees of non-stationarity.
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