Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm

• URL: http://arxiv.org/abs/2509.23135v3
• Date: Mon, 13 Oct 2025 15:33:42 GMT
• Title: Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm
• Authors: Yang Chen, Menglin Zou, Jiaqi Zhang, Yitan Zhang, Junyi Yang, Gael Gendron, Libo Zhang, Jiamou Liu, Michael J. Witbrock,
• Abstract summary: Inverse Reinforcement Learning (IRL) learns a reward function to explain expert demonstrations.<n>Modern IRL methods often use the adversarial (minimax) formulation that alternates between reward and policy optimization.<n>Recent non-adversarial IRL approaches improve stability by jointly learning reward and policy via energy-based formulations.
• Score: 24.126318783226598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse Reinforcement Learning (IRL) learns a reward function to explain expert demonstrations. Modern IRL methods often use the adversarial (minimax) formulation that alternates between reward and policy optimization, which often lead to unstable training. Recent non-adversarial IRL approaches improve stability by jointly learning reward and policy via energy-based formulations but lack formal guarantees. This work bridges this gap. We first present a unified view showing canonical non-adversarial methods explicitly or implicitly maximize the likelihood of expert behavior, which is equivalent to minimizing the expected return gap. This insight leads to our main contribution: Trust Region Reward Optimization (TRRO), a framework that guarantees monotonic improvement in this likelihood via a Minorization-Maximization process. We instantiate TRRO into Proximal Inverse Reward Optimization (PIRO), a practical and stable IRL algorithm. Theoretically, TRRO provides the IRL counterpart to the stability guarantees of Trust Region Policy Optimization (TRPO) in forward RL. Empirically, PIRO matches or surpasses state-of-the-art baselines in reward recovery, policy imitation with high sample efficiency on MuJoCo and Gym-Robotics benchmarks and a real-world animal behavior modeling task.

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