In-Person Poster presentation / top 5% paper

Extreme Q-Learning: MaxEnt RL without Entropy

Divyansh Garg · Joey Hejna · Matthieu Geist · Stefano Ermon

MH1-2-3-4 #97

Keywords: [ Reinforcement Learning ] [ reinforcement learning ] [ gumbel ] [ extreme value analysis ] [ maximum entropy rl ] [ statistical learning ] [ offline reinforcement learning ]


Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by \emph{10+ points} on the challenging Franka Kitchen tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks. Visualizations and code can be found on our website.

Chat is not available.