November 01, 2021
In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In low-dimensional environments, where tabular parameterization is possible, count-based upper confidence bound (UCB) exploration methods achieve minimax near-optimal rates. However, it remains unclear how to efficiently implement UCB in realistic RL tasks that involve nonlinear function approximation. To address this, we propose a new exploration approach via maximizing the deviation of the occupancy of the next policy from the explored regions. We add this term as an adaptive regularizer to the standard RL objective to trade off between exploration and exploitation. We pair the new objective with a provably convergent algorithm, giving rise to a new intrinsic reward that adjusts existing bonuses. The proposed intrinsic reward is easy to implement and combine with other existing RL algorithms to conduct exploration. As a proof of concept, we evaluate the new intrinsic reward on tabular examples across a variety of model-based and model-free algorithms, showing improvements over count-only exploration strategies. When tested on navigation and locomotion tasks from MiniGrid and DeepMind Control Suite benchmarks, our approach significantly improves sample efficiency over state-of-the-art methods.
Publisher
NeurIPS
Research Topics
May 06, 2024
Haoyue Tang, Tian Xie
May 06, 2024
April 30, 2024
Mikayel Samvelyan, Minqi Jiang, Davide Paglieri, Jack Parker-Holder, Tim Rocktäschel
April 30, 2024
April 02, 2024
Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar
April 02, 2024
March 26, 2024
Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
March 26, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models