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Max Weltevrede

2 accepted papers

2026

Universal Value-Function Uncertainties

ICLR 2026poster

Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, and offline RL. While deep ensembles provide a robust method for quantifying value uncertainty, they come with significan…

Cited by 0SourceScholar
2025

How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning

NeurIPS 2025poster

In the zero-shot policy transfer setting in reinforcement learning, the goal is to train an agent on a fixed set of training environments so that it can generalise to similar, but unseen, testing environments. Previous work has shown that policy distillation after training can sometimes produce a po…

Cited by 0SourceScholar