Intelligent Switching for Reset-Free RL

Published: 16 Jan 2024, Last Modified: 16 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: Reset-Free RL
Submission Guidelines: I certify that this submission complies with the submission instructions as described on
TL;DR: Intelligently switching between controllers leads to state-of-the-art performance on Reset-Free RL.
Abstract: In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The 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 (forward) with learned resets by constructing a second (backward) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent’s confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: reinforcement learning
Submission Number: 3623