Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity
Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems, such as meta-learning, hierarchical task decomposition, and reinforcement learning from human feedback (RL-HF). Most of the bilevel RL algorithms are either not scalable because of using hypergradient with Hessian, or they suffer from high sample complexity because of using penalty-based approximation methods. In this work, we propose a hypergradient-based bilevel RL algorithm using the optimality of the Boltzmann policy for the entropy regularized discounted RL objective function. Our proposed algorithm is Hessian-free and obtains an iter...
| Authors | Naman Saxena, Mudit Gaur, Vaneet Aggarwal |
|---|---|
| Primary category | cs.LG |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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