Filippos Christianos
Noah's Ark Lab, Huawei, London, UK.
Previously at University of Edinburgh and NVIDIA Research.
I completed my PhD in the CDT for Robotics and Autonomous Agents, advised by Stefano Albrecht in the (University of Edinburgh) as a member of the Autonomous Agents Research Group.
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.
news
Jan 1, 2024 | “Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks “ has been accepted in ICRA! It discusses how LLMs can be used to guide exploration in long-horizon, sparse-reward tasks. |
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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). |
selected publications
- TMLRPareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement LearningTransactions on Machine Learning Research, 2023
- NeurIPS
- NeurIPSBenchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksIn Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021