FisherShield: Sequential D-Optimal Anti-Rewards for Deployment-Time Reward Privacy in Reinforcement Learning
Reinforcement learning is increasingly deployed in autonomous systems whose reward functions encode operational priorities and decision-making objectives. However, observable behavior can enable external observers to infer sensitive reward parameters through inverse reinforcement learning. Existing deployment-time defe...