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David J Wu

8 accepted papers

2024

The Update-Equivalence Framework for Decision-Time Planning

ICLR 2024poster

The process of revising (or constructing) a policy at execution time---known as decision-time planning---has been key to achieving superhuman performance in perfect-information games like chess and Go. A recent line of work has extended decision-time planning to imperfect-information games, leading…

Cited by 5SourcePDFScholar
2023

Abstracting Imperfect Information Away from Two-Player Zero-Sum Games

ICML 2023poster

In their seminal work, Nayyar et al. (2013) showed that imperfect information can be abstracted away from common-payoff games by having players publicly announce their policies as they play. This insight underpins sound solvers and decision-time planning algorithms for common-payoff games. Unfortuna…

Cited by 8SourcePDFScholar
2023

Adversarial Diversity in Hanabi

ICLR 2023top-25%

Many Dec-POMDPs admit a qualitatively diverse set of ''reasonable'' joint policies, where reasonableness is indicated by symmetry equivariance, non-sabotaging behaviour and the graceful degradation of performance when paired with ad-hoc partners. Some of the work in diversity literature is concerned…

Cited by 19SourcePDFScholar
2023

Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning

ICLR 2023top-5%

No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is ins…

Cited by 55SourcePDFScholar
2022

A Fine-Tuning Approach to Belief State Modeling

ICLR 2022poster

We investigate the challenge of modeling the belief state of a partially observable Markov system, given sample-access to its dynamics model. This problem setting is often approached using parametric sequential generative modeling methods. However, these methods do not leverage any additional comput…

Cited by 13SourcePDFScholar
2022

Modeling Strong and Human-Like Gameplay with KL-Regularized Search

ICML 2022spotlight

We consider the task of accurately modeling strong human policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predicting human actions but may not match the strength of expert humans (e.g., by sometimes committing blunders), while sel…

2022

Self-Explaining Deviations for Coordination

NeurIPS 2022accept

Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which humans are able to discover self-explaining deviations (SEDs). SEDs are actions that deviate from the common understanding o…

Cited by 1SourcePDFScholar