papers
Publications (2)
cs.LG2026
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour +6
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed nu…
eess.SY2020
Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL
Eugene Vinitsky, Nathan Lichtle, Kanaad Parvate +1
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the proble…