Reward-Free Exploration for Reinforcement Learning

Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To isolate the challenges of exploration, we propose a new “reward-free RL” framework. In the exploration phase, the agent first collects trajectories from an MDPM without aprespecified reward function. After exploration, it is tasked with computing near-optimal policies under for M for a collection of given reward functions. This frame work is particularly suitable when there are many reward functions of interest, or when the reward function is shaped by an external agent to elicit desired behavior. We give an efficient algorithm that conducts Õ(S₂Apoly(H)|ϵ²)episodes of exploration and returns-suboptimal policies for an arbitrary number of reward functions. We achieve this by finding exploratory policies that visit each “significant” state with probability proportional to its maximum visitation probability under any possible policy. Moreover, our planning procedure can be instantiated by any black-box approximate planner, such as value iteration or natural policy gradient. We also give a nearly-matching Ω(S²AH²|ϵ²) lower bound, demonstrating the near-optimality of our algorithm in this setting.