Reinforcement learning improves safety by limiting risky decisions
Variational Consequence-Driven Offline Reinforcement Learning
Machine Learning
Summary
Offline reinforcement learning can struggle when small changes in actions lead to big changes in outcomes, making it hard for the algorithm to learn effectively from past data. The authors address this by focusing on how actions affect future states, not just matching previous actions. They propose a new method called Structural Behavior Regularization that uses a clever mathematical shortcut to keep the learned policy close to safe behaviors in tricky situations. Their experiments show this approach performs better than existing methods on common offline learning tests.
What this means in practice
- •For robotics engineers: Enhance robot control systems by preventing unsafe actions that lead to unfamiliar states during offline training.
- •For autonomous vehicle software teams: Improve offline learning of driving policies by adaptively restricting risky maneuvers likely to cause rare or unseen scenarios.
Authors
Wen Jiang, Ke Jiang, Xiaoyang Tan
Abstract
Offline reinforcement learning (RL) typically mitigates distribution shift by imposing divergence constraints on the action distributions of the learned and behavior policies. However, this paradigm cannot ensure the underlying dynamic consistency structure: even minor action deviations can lead to drastically different transitions, inadvertently driving the agent away from the offline dataset. To address this, we explore policy constraints through their induced state-consequence distributions rather than relying on pure action-distribution matching. Nevertheless, directly enforcing such consequence-aware constraints is computationally intractable without access to the true underlying dynamics model. To bypass this issue, we propose Structural Behavior Regularization (StBR), which replaces the intractable objective with a closed-form latent divergence. Structuring this latent space to reflect transition dynamics renders the latent divergence a theoretically bounded surrogate for the true consequence-distribution divergence. By regularizing the learned policy with this latent divergence, StBR adaptively tightens the behavior constraint in dynamics-sensitive regions, effectively preventing deviations outside the offline dataset. Empirically, we demonstrate that StBR achieves superior performance across diverse offline RL benchmarks.