Nonstationary Reinforcement Learning with Linear Function Approximation
This work addresses the problem of adapting reinforcement learning to changing environments for researchers and practitioners, representing an incremental advance by extending existing methods to handle nonstationarity with theoretical guarantees.
The paper tackles reinforcement learning in nonstationary environments with linear function approximation, where reward and transition functions drift over time, by proposing algorithms (LSVI-UCB-Restart and Ada-LSVI-UCB-Restart) that achieve bounded dynamic regret and establish minimax lower bounds, with numerical experiments validating their effectiveness.
We consider reinforcement learning (RL) in episodic Markov decision processes (MDPs) with linear function approximation under drifting environment. Specifically, both the reward and state transition functions can evolve over time but their total variations do not exceed a $\textit{variation budget}$. We first develop $\texttt{LSVI-UCB-Restart}$ algorithm, an optimistic modification of least-squares value iteration with periodic restart, and bound its dynamic regret when variation budgets are known. Then we propose a parameter-free algorithm $\texttt{Ada-LSVI-UCB-Restart}$ that extends to unknown variation budgets. We also derive the first minimax dynamic regret lower bound for nonstationary linear MDPs and as a byproduct establish a minimax regret lower bound for linear MDPs unsolved by Jin et al. (2020). Finally, we provide numerical experiments to demonstrate the effectiveness of our proposed algorithms.