Intelligent Switching for Reset-Free RL

arXiv:2405.01684v1 Announce Type: new
Abstract: In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resetting} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We …

abstract agent agents arxiv cs.ai cs.lg free human intelligent reinforcement reinforcement learning simulation train type world