Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
This addresses the problem of ensuring AI control policies are both optimal and behavior-consistent for applications like robotics and autonomous systems, representing an incremental improvement by combining reinforcement learning with imitation learning constraints.
The paper tackles the challenge of learning high-performance control policies that remain consistent with expert behavior in robotics, proposing a behavior-constrained reinforcement learning framework with receding-horizon credit assignment. The result is that learned policies achieve competitive lap times while maintaining close alignment with expert driving behavior, outperforming baselines in performance and imitation quality, as validated in high-fidelity race car simulation and human-grounded evaluations.
Learning high-performance control policies that remain consistent with expert behavior is a fundamental challenge in robotics. Reinforcement learning can discover high-performing strategies but often departs from desirable human behavior, whereas imitation learning is limited by demonstration quality and struggles to improve beyond expert data. We propose a behavior-constrained reinforcement learning framework that improves beyond demonstrations while explicitly controlling deviation from expert behavior. Because expert-consistent behavior in dynamic control is inherently trajectory-level, we introduce a receding-horizon predictive mechanism that models short-term future trajectories and provides look-ahead rewards during training. To account for the natural variability of human behavior under disturbances and changing conditions, we further condition the policy on reference trajectories, allowing it to represent a distribution of expert-consistent behaviors rather than a single deterministic target. Empirically, we evaluate the approach in high-fidelity race car simulation using data from professional drivers, a domain characterized by extreme dynamics and narrow performance margins. The learned policies achieve competitive lap times while maintaining close alignment with expert driving behavior, outperforming baseline methods in both performance and imitation quality. Beyond standard benchmarks, we conduct human-grounded evaluation in a driver-in-the-loop simulator and show that the learned policies reproduce setup-dependent driving characteristics consistent with the feedback of top-class professional race drivers. These results demonstrate that our method enables learning high-performance control policies that are both optimal and behavior-consistent, and can serve as reliable surrogates for human decision-making in complex control systems.