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Caroline Horsch

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
2021

PettingZoo: Gym for Multi-Agent Reinforcement Learning

NeurIPS 2021poster

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle (``"AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Re…