Trust Region Inverse Reinforcement Learning with Local Policy Updates
A recent study available on arXiv (2605.11020) introduces an approach to inverse reinforcement learning (IRL) that ensures consistent enhancement of both the policy and reward function without the necessity of addressing a complete reinforcement learning (RL) problem in every iteration. Traditional dual-ascent IRL offers monotonic performance but demands solving an RL problem at each step to derive dual gradients. While adversarial techniques eliminate this requirement, they compromise on stability and the monotonicity of dual improvement. The crucial theoretical breakthrough is that a trust-region-optimal policy for updating the reward function can also be globally optimal for a smaller directional update, allowing for explicit dual optimization through localized policy adjustments.
Key facts
- arXiv paper 2605.11020 proposes trust region inverse reinforcement learning.
- Method bridges classical dual-ascent IRL and adversarial IRL.
- Achieves monotonic improvement without solving full RL each iteration.
- Uses local policy updates for explicit dual optimization.
- Key insight: trust-region-optimal policy for a reward update is globally optimal for a smaller update.
- Classical IRL requires solving RL problem each iteration.
- Adversarial IRL uses discriminator for rewards but lacks monotonic improvement.
- Paper provides theoretical guarantee for monotonic dual improvement.
Entities
Institutions
- arXiv