A view on learning robust goal-conditioned value functions: Interplay between RL and MPC

Abstract

Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields have had independently across numerous domains, there is growing interest in combining the general-purpose learning capability of RL with the safety and robustness features of MPC. To this end, this paper presents a tutorial-style treatment of RL and MPC, treating them as alternative approaches to solving Markov decision processes. In our formulation, RL aims to learn a global value function through offline exploration in an uncertain environment, whereas MPC constructs a local value function through online optimization. This local-global perspective suggests new ways to design policies that combine robustness and goal-conditioned learning. Robustness is incorporated into the RL and MPC pipelines through a scenario-based approach. Goal-conditioned learning aims to alleviate the burden of engineering a reward function for RL. Combining the two leads to a single policy that unites a robust, high-level RL terminal value function with short-term, scenario-based MPC planning for reliable constraint satisfaction. This approach leverages the benefits of both RL and MPC, the effectiveness of which is demonstrated on classical control benchmarks.

Postprint; 37 pages

References in corpus (6)

• Conservative Q-Learning for Offline Reinforcement Learning
• Machine learning for industrial sensing and control: A survey and practical perspective
• Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
• Deep Value Model Predictive Control
• Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
• Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification