Constrained Reinforcement Learning Under Model Mismatch

TL;DR

This paper introduces a Robust Constrained Policy Optimization algorithm for reinforcement learning that effectively handles model mismatch between training and deployment environments, ensuring reward optimization and constraint satisfaction.

Contribution

It presents the first large/continuous state space algorithm with theoretical guarantees for worst-case reward and constraint violation under model uncertainty.

Findings

RCPO outperforms existing methods in constrained RL tasks.

Theoretical guarantees ensure reliable performance during training.

Effective in large/continuous state spaces.

Abstract

Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may easily violate constraints that were originally satisfied during training because there might be model mismatch between the training and real environments. To address the above challenge, we formulate the problem as constrained RL under model uncertainty, where the goal is to learn a good policy that optimizes the reward and at the same time satisfy the constraint under model mismatch. We develop a Robust Constrained Policy Optimization (RCPO) algorithm, which is the first algorithm that applies to large/continuous state space and has theoretical guarantees on worst-case reward improvement and constraint violation at each iteration during the training. We demonstrate the effectiveness of our algorithm on a set of RL tasks with constraints.