Extreme Q-Learning: MaxEnt RL without Entropy

Stanford University1         Google2
*Equal Contribution



TLDR: A novel framework for Q-learning that models the maximal soft-values without needing to sample from a policy.


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 (XQL) 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.

Key Idea: Gumbel Regression

TLDR: The core of our approach is fitting Gumbel distribution \(\mathcal{G}(\mu, \beta)\) to the data to introduce Gumbel regression (or Extremal regression), a new technique which models the extreme values of a distribution.

This is similar to fitting a Gaussian distribution to the data, but instead of modeling the mean (i.e. least squares regression) it fits the Log-Sum-Exp or the Log-Partition function of the data.

For a temperature \(\beta\), Gumbel regression estimates the operator \(\beta \log \mathbb{E}[e^{X/\beta}] \) or the Log-Partition function over samples drawn from a distribution \(X\). This is a central quantity of interest in Statistics as well as Physical Sciences, and it's accurate calculation has important applications in Probabilistic Modeling, Bayesian Learning and Information Theory, such as in calculating maginal distributions.

Nevertheless, it is very difficult to estimate in continuous spaces and usually assumed as an intractable quantity. This has led to a host of variational inference methods such as VAEs, that use approximations to side-step calculating it. Gumbel Regression enables for the first time, exact estimation of the Log-Partition function by using simple gradient descent.

By controlling the temperature \(\beta\), Gumbel regression interpolates between the the max (\(\beta=0\)) and the mean (\(\beta=\infty\)) of a distribution \(X\), and provides a robust estimator for the extremal values of a distribution. Finally, Gumbel Regression admits to tight PAC learning bounds and has a bound approximation error on a finite dataset (Section 3 of the paper).


Our Gumbel regression loss function can be used to directly fit the Log-Sum-Exp of the Q-values, yielding the soft-optimal value function \(V^* = LogSumExp(Q)\). Then, we can use Q-iteration even in high-dimensional continuous action spaces to find the optimal MaxEnt policy. This general algorithm works well in both online, and offline settings.

For online RL, it can be used to extend existing algorithms like SAC and TD3, with moderate performance gains. On offline RL, it outperforms existing approaches, and obtains SOTA on D4RL benchmarks. Below we provide a high-level overview:

Offline Results

(Above) XQL reaching state of the art results on the Offline D4RL Benchmark

XQL on Franka Kitchen
IQL on Franka Kitchen

Online Results

X-TD3 shows moderate gains on DM Control Tasks compared to standard TD3.

X-TD3 on Quadruped Run (Reward 437)
TD3 on Quadruped Run (Reward 293)

X-TD3 on Hopper Hop (Reward 71)
TD3 on Hopper Hop (Reward 20)


	title={Extreme Q-Learning: MaxEnt Reinforcement Learning Without Entropy},
	url = {https://arxiv.org/abs/2301.02328},
  	author = {Garg, Divyansh and Hejna, Joey and Geist, Matthieu and Ermon, Stefano},
	publisher = {arXiv},
  	year = {2023},