Logarithmic Regret for Reinforcement Learning with Linear Function Approximation

Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining $sqrt{T}$-type regret bound, where $T$ is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently proposed linear MDP assumptions provided that there exists a positive sub-optimality gap for the optimal action-value function. More specifically, under the linear MDP assumption (Jin et al. 2019), the LSVI-UCB algorithm can achieve $tilde{O}(d^{3}H^5/text{gap}_{text{min}}cdot log(T))$ regret; and under the linear mixture MDP assumption (Ayoub et al. 2020), the UCRL-VTR algorithm can achieve $tilde{O}(d^{2}H^5/text{gap}_{text{min}}cdot log^3(T))$ regret, where $d$ is the dimension of feature mapping, $H$ is the length of episode, $text{gap}_{text{min}}$ is the minimal sub-optimality gap, and $tilde O$ hides all logarithmic terms except $log(T)$. To the best of our knowledge, these are the first logarithmic regret bounds for RL with linear function approximation. We also establish gap-dependent lower bounds for the two linear MDP models.