MaxMin-Novelty: Maximizing Novelty via Minimizing the State-Action Values in Deep Reinforcement LearningDownload PDF


22 Sept 2022, 12:39 (modified: 18 Nov 2022, 13:03)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Abstract: Reinforcement learning research has achieved high acceleration in its progress starting from the initial installation of deep neural networks as function approximators to learn policies that make sequential decisions in high-dimensional state representation MDPs. While several consecutive barriers have been broken in deep reinforcement learning research (i.e. learning from high-dimensional states, learning purely via self-play), several others still stand. On this line, the question of how to explore in high-dimensional complex MDPs is a well-understudied and ongoing open problem. To address this, in our paper we propose a unique exploration technique based on maximization of novelty via minimization of the state-action value function (MaxMin Novelty). Our method is theoretically well motivated, and comes with zero computational cost while leading to significant sample efficiency gains in deep reinforcement learning training. We conduct extensive experiments in the Arcade Learning Environment with high-dimensional state representation MDPs. We show that our technique improves the human normalized median scores of Arcade Learning Environment by 248% in the low-data regime.
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.
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
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)
17 Replies