Previously at NVIDIA Research.
My PhD research is in the area of Multi-Agent Deep Reinforcement Learning. In particular, I study how multiple agents can efficiently explore and learn in environments with sparse rewards.
I am the author and maintainer of the Multi-Robot Warehouse environment for multi-agent RL research. I also developed and maintain the Python version of Level-based Foraging. Our group has been using both environments to develop new and exciting algorithms for MARL. I am the first author of two such algorithms: Shared Experience Actor-Critic (SEAC), and Selective Parameter Sharing (SePS) that have been published in NeurIPS (2020) and ICML (2021) respectively.
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.
|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).|
- 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