Safe Model-Based Reinforcement Learning With an Uncertainty-Aware Reachability Certificate

Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such as robotics. Among all safe RL approaches, model-based methods reduce training time violations further due to their high sample efficiency. However, lacking safety robustness against the model uncertainties remains an issue in safe model-based RL, especially in training time safety. In this paper, we propose a distributional reachability certificate (DRC) and its Bellman equation to address model uncertainties and characterize robust persistently safe states. Furthermore, we build a safe RL framework to resolve constraints required by the DRC and its corresponding shield policy. We also devise a line search method to maintain safety and reach higher returns simultaneously while leveraging the shield policy. Comprehensive experiments on classical benchmarks such as constrained tracking and navigation indicate that the proposed algorithm achieves comparable returns with much fewer constraint violations during training. Our code is available at https://github.com/ManUtdMoon/Distributional-Reachability-Policy-Optimization. Note to Practitioners—Although it has been proven that RL can be applied in complex robotics control tasks, the training process of an RL control policy induces frequent failures because the agent needs to learn safety through constraint violations. This issue hinders the promotion of RL because a large amount of failure of robots is too expensive to afford. This paper aims to reduce the training-time violations of RL-based control methods, enabling RL to be leveraged in a broader application area. To achieve the goal, we first introduce a safety quantity describing the distribution of potential constraint violations in the long term. By imposing constraints on the quantile of the safety distribution, we can realize safety robust to the model uncertainty, which is necessary for real-world robot learning with environment uncertainty. Second, we further devise a shield policy aiming to minimize the constraint violation. The policy will intervene when the agent is about to violate state constraints, further enhancing exploration safety. Third, we implement a line search method to find an action pursuing near-optimal performance when fulfilling safety requirements strictly. Our experimental results indicate that the proposed algorithm reduces training-time violations significantly while maintaining competitive task performance. We make a step towards applying RL safely in real-world tasks. Our future work includes conducting physical verification on real robots to evaluate the algorithm and improving safety further by starting from an initially safe control policy that comes from domain knowledge.

I. Introduction

Reinforcement learning (RL) has achieved success across different automated control tasks such as robotics locomotion [1], navigation [2] and transportation management [3], [4]. However, the trial-and-error process of RL hinders its application in more real-world tasks due to the large number of failures brought by unconstrained policies, threatening the safety of users and systems. Therefore, safe RL [5] is proposed to impose constraints on agents and enhance the safety of policies both after convergence and during the training process. The training time safety issue, also called safe exploration (i.e., reducing the number of constraint violations during learning), is thought to be challenging and significant, especially when the dynamics of the environment are unknown. In this paper, we not only focus on the safe RL problem subject to certain constraints after convergence, but also take a step towards safe exploration.