DeepAI AI Chat
Log In Sign Up

Mutual Information Regularized Offline Reinforcement Learning

by   Xiao Ma, et al.

Offline reinforcement learning (RL) aims at learning an effective policy from offline datasets without active interactions with the environment. The major challenge of offline RL is the distribution shift that appears when out-of-distribution actions are queried, which makes the policy improvement direction biased by extrapolation errors. Most existing methods address this problem by penalizing the policy for deviating from the behavior policy during policy improvement or making conservative updates for value functions during policy evaluation. In this work, we propose a novel MISA framework to approach offline RL from the perspective of Mutual Information between States and Actions in the dataset by directly constraining the policy improvement direction. Intuitively, mutual information measures the mutual dependence of actions and states, which reflects how a behavior agent reacts to certain environment states during data collection. To effectively utilize this information to facilitate policy learning, MISA constructs lower bounds of mutual information parameterized by the policy and Q-values. We show that optimizing this lower bound is equivalent to maximizing the likelihood of a one-step improved policy on the offline dataset. In this way, we constrain the policy improvement direction to lie in the data manifold. The resulting algorithm simultaneously augments the policy evaluation and improvement by adding a mutual information regularization. MISA is a general offline RL framework that unifies conservative Q-learning (CQL) and behavior regularization methods (e.g., TD3+BC) as special cases. Our experiments show that MISA performs significantly better than existing methods and achieves new state-of-the-art on various tasks of the D4RL benchmark.


page 1

page 2

page 3

page 4


Behavior Prior Representation learning for Offline Reinforcement Learning

Offline reinforcement learning (RL) struggles in environments with rich ...

Model-Based Offline Meta-Reinforcement Learning with Regularization

Existing offline reinforcement learning (RL) methods face a few major ch...

APS: Active Pretraining with Successor Features

We introduce a new unsupervised pretraining objective for reinforcement ...

Regularized Behavior Value Estimation

Offline reinforcement learning restricts the learning process to rely on...

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients

As reinforcement learning techniques are increasingly applied to real-wo...

Deep Reinforcement and InfoMax Learning

Our work is based on the hypothesis that a model-free agent whose repres...

A Regularized Implicit Policy for Offline Reinforcement Learning

Offline reinforcement learning enables learning from a fixed dataset, wi...