DeepMind Lab2D

We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, but it may also be useful beyond that particular subfield.

[1]  Joel Z. Leibo,et al.  Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research , 2019, ArXiv.

[2]  Inioluwa Deborah Raji,et al.  Model Cards for Model Reporting , 2018, FAT.

[3]  Joshua B. Tenenbaum,et al.  Help or Hinder: Bayesian Models of Social Goal Inference , 2009, NIPS.

[4]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[5]  Tuomas Sandholm Solving imperfect-information games , 2015, Science.

[6]  Shane Legg,et al.  Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents , 2018, ArXiv.

[7]  John C. Harsanyi,et al.  Games with Incomplete Information Played by "Bayesian" Players, I-III: Part I. The Basic Model& , 2004, Manag. Sci..

[8]  Tor Lattimore,et al.  Behaviour Suite for Reinforcement Learning , 2019, ICLR.

[9]  Tom Schaul,et al.  A video game description language for model-based or interactive learning , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[10]  Peter Henderson,et al.  Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning , 2020, ArXiv.

[11]  C. Shannon A chess-playing machine. , 1950, Scientific American.

[12]  Marwan Mattar,et al.  Unity: A General Platform for Intelligent Agents , 2018, ArXiv.

[13]  Alexander Peysakhovich,et al.  Maintaining cooperation in complex social dilemmas using deep reinforcement learning , 2017, ArXiv.

[14]  F. Gage,et al.  Neural consequences of enviromental enrichment , 2000, Nature Reviews Neuroscience.

[15]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[16]  Joel Z. Leibo,et al.  OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning , 2020, ICML.

[17]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[18]  Richard S. Sutton,et al.  Using Predictive Representations to Improve Generalization in Reinforcement Learning , 2005, IJCAI.

[19]  M. D. Molina,et al.  The Basic Model , 2014 .

[20]  David C. Parkes,et al.  The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies , 2020, ArXiv.

[21]  Joel Z. Leibo,et al.  Multi-agent Reinforcement Learning in Sequential Social Dilemmas , 2017, AAMAS.

[22]  Yuval Tassa,et al.  MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Joelle Pineau,et al.  RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms , 2018 .

[24]  Wojciech Jaskowski,et al.  ViZDoom: A Doom-based AI research platform for visual reinforcement learning , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[25]  Igor Mordatch,et al.  Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents , 2019, ArXiv.

[26]  Weinan Zhang,et al.  MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence , 2017, AAAI.

[27]  Tom Mitchell,et al.  Jelly Bean World: A Testbed for Never-Ending Learning , 2020, ICLR.