Near-optimal Policy Identification in Active Reinforcement Learning

Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a generative model. We propose the AE-LSVI algorithm for bestpolicy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy uniformly over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required. Reinforcement learning (RL) algorithms are increasingly applied to complex domains such as robotics (Kober et al., 2013), magnetic tokamaks (Seo et al., 2021; Degrave et al., 2022), and molecular search (Simm et al., 2020a;b). A central challenge in such environments is that data acquisition is often a time-consuming and expensive process, or may be infeasible due to safety considerations.