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

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

RANK_REASON 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]

Read on arXiv cs.LG →

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

• paper
• other

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