MinMaxMin $Q$-learning

Nitsan Soffair; Shie Mannor

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arXiv · 2402.05951

MinMaxMin $Q$-learning

Nitsan Soffair·Shie Mannor

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Abstract

MinMaxMin $Q$-learning is a novel optimistic Actor-Critic algorithm that addresses the problem of overestimation bias ($Q$-estimations are overestimating the real $Q$-values) inherent in conservative RL algorithms. Its core formula relies on the disagreement among $Q$-networks in the form of the min-batch MaxMin $Q$-networks distance which is added to the $Q$-target and used as the priority experience replay sampling-rule. We implement MinMaxMin on top of TD3 and TD7, subjecting it to rigorous testing against state-of-the-art continuous-space algorithms-DDPG, TD3, and TD7-across popular MuJoCo and Bullet environments. The results show a consistent performance improvement of MinMaxMin over DDPG, TD3, and TD7 across all tested tasks.

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BibTeX RIS

Nitsan Soffair, Shie Mannor. 2024-06-02. MinMaxMin $Q$-learning. https://arxiv.org/abs/2402.05951

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