Factored Adaptation for Non-Stationary Reinforcement Learning
This addresses the challenge of non-stationarity in RL for real-world applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of non-stationarity in reinforcement learning environments and objectives by proposing FANS-RL, a factored adaptation approach that models changes as individual latent factors and causal graphs, which outperforms existing methods in return, representation compactness, and robustness.
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.