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Reinforcement learning (RL) so far has limited real-world applications. Onekey challenge is that typical RL algorithms heavily rely on a reset mechanismto sample proper initial states; these reset mechanisms, in practice, areexpensive to implement due to the need for human intervention or heavilyengineered environments. To make learning more practical, we propose a genericno-regret reduction to systematically design reset-free RL algorithms. Ourreduction turns the reset-free RL problem into a two-player game. We show thatachieving sublinear regret in this two-player game would imply learning apolicy that has both sublinear performance regret and sublinear total number ofresets in the original RL problem. This means that the agent eventually learnsto perform optimally and avoid resets. To demonstrate the effectiveness of thisreduction, we design an instantiation for linear Markov decision processes,which is the first provably correct reset-free RL algorithm.

Provable Reset-free Reinforcement Learning by No-Regret Reduction