New Malliavin calculus method estimates counterfactual gradients for adaptive IRL

Researchers have developed a novel passive algorithm for adaptive inverse reinforcement learning (IRL) that reconstructs a forward learner's loss function by observing its gradients. This new method utilizes Malliavin calculus to efficiently estimate counterfactual gradients, which are crucial but difficult to obtain in passive IRL scenarios. By reformulating the conditioning as a ratio of unconditioned expectations involving Malliavin quantities, the algorithm achieves standard estimation rates and offers a concrete approach for this complex gradient estimation problem. AI

影响 Introduces a new mathematical technique to improve gradient estimation in reinforcement learning, potentially enhancing the efficiency of learning agent behaviors.

排序理由 This is a research paper detailing a novel algorithmic approach for adaptive inverse reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Inverse Reinforcement Learning
• Langevin
• Luke Snow
• Malliavin Calculus
• Monte Carlo
• Reinforcement Learning

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