arXiv:2606.28152v1 Announce Type: new Abstract: We propose KL-Coupled Policy Regularization (KCPR), a policy coordination framework for Reward-Punishment Reinforcement Learning (RPRL). Based on KCPR, we derive KL-Coupled Soft Optimality (KCSO) and develop its deep realization, klDMP. Unlike existing RPRL approaches that optimize reward-seeking and punishment-related policies largely independently, KCPR enables direct interactions between companion policies by treating each as a dynamically learned prior for the other. KCSO yields coupled soft-optimal policies and KL-regularized Bellman operato
The continuous drive for more sophisticated and robust AI agents necessitates novel approaches to reinforcement learning that can handle complex reward and punishment dynamics.
This research provides a more integrated framework for AI agents to process positive and negative reinforcement, potentially leading to more adaptable and ethically aligned autonomous systems.
Existing RPRL methods, which often treat reward and punishment policies independently, could be superseded by integrated approaches like KCPR, fostering more nuanced AI decision-making.
- · AI researchers
- · Robotics developers
- · AI ethics and safety organizations
- · Developers relying solely on independent RPRL frameworks
Improved performance and robustness of AI agents in complex environments.
Faster development and deployment of autonomous systems with enhanced learning capabilities.
Potential for new applications in areas requiring delicate balancing of incentives, such as personalized medicine or adaptive defense systems.
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Read at arXiv cs.LG