Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
This addresses the problem of handling time-varying environments in RL for applications like robotics or finance, representing an incremental advance with a novel confidence widening technique.
The paper tackles reinforcement learning in non-stationary Markov decision processes where rewards and transitions change over time, developing algorithms (SWUCRL2-CW and BORL) that achieve dynamic regret bounds with known or adaptively tuned variation budgets.
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the Sliding Window Upper-Confidence bound for Reinforcement Learning with Confidence Widening (SWUCRL2-CW) algorithm, and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the Bandit-over-Reinforcement Learning (BORL) algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound, but in a parameter-free manner, i.e., without knowing the variation budgets. Notably, learning non-stationary MDPs via the conventional optimistic exploration technique presents a unique challenge absent in existing (non-stationary) bandit learning settings. We overcome the challenge by a novel confidence widening technique that incorporates additional optimism.