Extreme Q-Learning: MaxEnt RL without Entropy

• URL: http://arxiv.org/abs/2301.02328v1
• Date: Thu, 5 Jan 2023 23:14:38 GMT
• Title: Extreme Q-Learning: MaxEnt RL without Entropy
• Authors: Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon
• Abstract summary: Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains. We introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT) Using EVT, we derive our Extreme Q-Learning framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms.
• Score: 88.97516083146371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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 10+ points on some tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks.

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