Modeling Agents with Probabilistic Programs

This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases and bounded rationality.

Agents are implemented as differentiable functional programs in a probabilistic programming language based on Javascript. Agents plan by recursively simulating their future selves or by simulating their opponents in multi-agent games. Our agents and environments run directly in the browser and are easy to modify and extend.

For more information about this project, contact Owain Evans.

Table of contents

Citation

Please cite this book as:

Owain Evans, Andreas Stuhlmüller, John Salvatier, and Daniel Filan (electronic). Modeling Agents with Probabilistic Programs. Retrieved from http://agentmodels.org . [bibtex]

@misc{agentmodels, title = {{Modeling Agents with Probabilistic Programs}}, author = {Evans, Owain and Stuhlm\"{u}ller, Andreas and Salvatier, John and Filan, Daniel}, year = {2017}, howpublished = {\url{http://agentmodels.org}}, note = {Accessed: } }

Open source

Book content

Markdown code for the book chapters

Markdown code for the book chapters WebPPL

A probabilistic programming language for the web

A probabilistic programming language for the web WebPPL-Agents

A library for modeling MDP and POMDP agents in WebPPL



Acknowledgments

We thank Noah Goodman for helpful discussions, all WebPPL contributors for their work, and Long Ouyang for webppl-viz. This work was supported by Future of Life Institute grant 2015-144846 and by the Future of Humanity Institute (Oxford).