I am working on Multi-Agent Deep Reinforcement Learning. I am co-author of the Multi-Agent Reinforcement Learning: Foundations and Modern Approaches textbook which serves as an introduction to MARL. Online version of the book is available for free! I am also the maintainer of the book’s code base found here: fast-marl.
I also authored and maintain the Multi-Robot Warehouse environment for multi-agent RL research, and the Python version of Level-based Foraging. Finally, I am a co-author of E-PyMARL, a library for MARL which has been widely used by the community.
Keywords: Machine Learning, Deep Reinforcement Learning (RL), Multi-agent Systems, Exploration in RL.
|Oct 29, 2023||Our paper “Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning” now accepted in TMLR! Pareto-AC learns Pareto-optimal Equilibria in many MARL environments and reaches new sota results.|
|May 29, 2023||📖: Pre-print version of our book on MARL was just released! Find it here: Multi-Agent Reinforcement Learning: Foundations and Modern Approaches|
|Jan 18, 2023||My NVIDIA internship resulted in “Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models” which was just accepted in ICRA 2023!|
|Oct 29, 2022||Preprints of my two new papers are online! My NVIDIA internship paper on AV occlusions and Pareto Actor-Critic: a new algorithm for MARL.|
|Jun 23, 2022||I joined NVIDIA Research for a three month internship on autonomous vehicles!|
|Dec 20, 2021||Our paper titled “Decoupling Exploitation and Intrinsically-Motivated Exploration in Reinforcement Learning” has been accepted in AAMAS 2022!|
|Sep 27, 2021||Another paper accepted at NeurIPS 2021: Agent Modelling under Partial Observability for Deep Reinforcement Learning.|
|Jul 29, 2021||Our benchmarking paper for MARL has been accepted at NeurIPS 2021: Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks.|
|May 10, 2021||Two new papers accepted at ICML 2021: Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing and Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning.|
|Dec 20, 2020||My blog post on two new environments for MARL has just been posted in our groups webpage.|
|Dec 8, 2020||Our paper, Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning, has been accepted and published in Neural Information Processing Systems (NeurIPS 2020).|
- TMLRPareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement LearningTransactions on Machine Learning Research, 2023
- ICMLScaling Multi-Agent Reinforcement Learning with Selective Parameter SharingIn Proceedings of the 38th International Conference on Machine Learning, 2021
- NeurIPSBenchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksIn Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021
- ICRAPlanning with Occluded Traffic Agents using Bi-Level Variational Occlusion ModelsIn IEEE International Conference on Robotics and Automation, 2023