Search Results for author: William Macke

Found 6 papers, 2 papers with code

Testing the Effect of Code Documentation on Large Language Model Code Understanding

no code implementations3 Apr 2024 William Macke, Michael Doyle

Large Language Models (LLMs) have demonstrated impressive abilities in recent years with regards to code generation and understanding.

Code Generation Language Modelling +1

A Survey of Ad Hoc Teamwork Research

no code implementations16 Feb 2022 Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman, Elliot Fosong, William Macke, Mohan Sridharan, Peter Stone, Stefano V. Albrecht

Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination.

Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction

1 code implementation3 Dec 2021 Yulin Zhang, William Macke, Jiaxun Cui, Daniel Urieli, Peter Stone

This article establishes for the first time that a multiagent driving policy can be trained in such a way that it generalizes to different traffic flows, AV penetration, and road geometries, including on multi-lane roads.

Autonomous Vehicles

Expected Value of Communication for Planning in Ad Hoc Teamwork

no code implementations1 Mar 2021 William Macke, Reuth Mirsky, Peter Stone

We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly.

Scalable Multiagent Driving Policies For Reducing Traffic Congestion

1 code implementation26 Feb 2021 Jiaxun Cui, William Macke, Harel Yedidsion, Daniel Urieli, Peter Stone

Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles).

Transfer Reinforcement Learning

Evolutionary Optimization of Deep Learning Activation Functions

no code implementations17 Feb 2020 Garrett Bingham, William Macke, Risto Miikkulainen

The choice of activation function can have a large effect on the performance of a neural network.

Evolutionary Algorithms

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