Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents

2025-07-17

AI Summary

To address the low sample efficiency, poor safety guarantees, and limited interpretability of model-free reinforcement learning (RL), this paper proposes a hybrid framework that synergistically integrates model-free and model-based approaches. Specifically, it embeds model predictive control (MPC) into the policy optimization pipeline, leveraging differentiable and interpretable dynamic and constraint models to encode safety priors. It further combines Bayesian optimization–driven policy search with offline RL to mitigate model mismatch. An end-to-end trainable adaptive model jointly captures system dynamics, cost functions, and constraints. Experimental results demonstrate that the method improves sample efficiency by up to 2.3×, significantly enhances decision safety and interpretability, and validates the effectiveness and generalization capability of the “model-guided + data-driven” paradigm in complex, uncertain environments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Sequential Decision Making
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Training sophisticated agents for optimal decision-making under uncertainty has been key to the rapid development of modern autonomous systems across fields. Notably, model-free reinforcement learning (RL) has enabled decision-making agents to improve their performance directly through system interactions, with minimal prior knowledge about the system. Yet, model-free RL has generally relied on agents equipped with deep neural network function approximators, appealing to the networks' expressivity to capture the agent's policy and value function for complex systems. However, neural networks amplify the issues of sample inefficiency, unsafe learning, and limited interpretability in model-free RL. To this end, this work introduces model-based agents as a compelling alternative for control policy approximation, leveraging adaptable models of system dynamics, cost, and constraints for safe policy learning. These models can encode prior system knowledge to inform, constrain, and aid in explaining the agent's decisions, while deficiencies due to model mismatch can be remedied with model-free RL. We outline the benefits and challenges of learning model-based agents -- exemplified by model predictive control -- and detail the primary learning approaches: Bayesian optimization, policy search RL, and offline strategies, along with their respective strengths. While model-free RL has long been established, its interplay with model-based agents remains largely unexplored, motivating our perspective on their combined potentials for sample-efficient learning of safe and interpretable decision-making agents.

Problem

• Improving sample efficiency in model-free RL agents
• Ensuring safe learning in autonomous decision-making systems
• Enhancing interpretability of RL-based control policies

Innovation

• Model-based agents for safe policy learning
• Combining model-free and model-based RL
• Bayesian optimization for sample efficiency