Abstract

 Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical work has suggested that model-free algorithms may require more samples to learn [7, 22]. The theoretical question of “whether model-free algorithms can be made sample efficient” is one of the most fundamental questions in RL, and remains unsolved even in the basic scenario with finitely many states and actions. We prove that, in  an episodic MDP setting, Q-learning with UCB exploration achieves regret , where S and A are the numbers of states and actions, H is the number of steps per episode, and T is the total number of steps. This sample efficiency matches the optimal  regret that can be achieved by any model-based approach, up to a single factor. To the best of  our knowledge, this is the first analysis in the model-free setting that establishes regret without requiring access to a “simulator.”