8 citations · 20 across the 6 of their papers we have counts for
7 papers
K-level Reasoning for Zero-Shot Coordination in Hanabi
Brandon Cui, Hengyuan Hu, Luis Pineda +1
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together. However, optimal SP polici…
Self-Explaining Deviations for Coordination
Hengyuan Hu, Samuel Sokota, David Wu +4
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 huma…
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
Chris Cummins, Bram Wasti, Jiadong Guo +9
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domain…
Learning Space Partitions for Path Planning
Kevin Yang, Tianjun Zhang, Chris Cummins +6
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function. Popular approaches like…
Off-Belief Learning
Hengyuan Hu, Adam Lerer, Brandon Cui +4
The standard problem setting in Dec-POMDPs is self-play, where the goal is to find a set of policies that play optimally together. Policies learned through self-play may adopt arbi…
Control-Aware Representations for Model-based Reinforcement Learning
Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh
A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE)…