38 citations · 62 across the 9 of their papers we have counts for
10 papers
Can Play That Game: Online Fitted -Iteration for Continuous-Action Zero-Sum Markov Games with Convex-Concave Function Approximation
Kushagra Gupta, Jingqi Li, Cade Armstrong +4
Zero-sum Markov games arise in a wide variety of sequential decision-making problems such as adversarial learning and planning against modeled uncertainties. However, prior work on…
More Information is Not Always Better: Connections between Zero-Sum Local Nash Equilibria in Feedback and Open-Loop Information Patterns
Kushagra Gupta, Ross Allen, David Fridovich-Keil +1
Non-cooperative dynamic game theory provides a principled approach to modeling sequential decision-making among multiple noncommunicative agents. A key focus has been on finding Na…
Distributed Online Planning for Min-Max Problems in Networked Markov Games
Alexandros E. Tzikas, Jinkyoo Park, Mykel J. Kochenderfer +1
Min-max problems are important in multi-agent sequential decision-making because they improve the performance of the worst-performing agent in the network. However, solving the mul…
Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions
Wei Xiao, Ross Allen, Daniela Rus
This paper addresses the problem of safety-critical control for non-affine control systems. It has been shown that optimizing quadratic costs subject to state and control constrain…
Learned Risk Metric Maps for Kinodynamic Systems
Ross Allen, Wei Xiao, Daniela Rus
We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high dimensional dynamical systems operating in unstructured, partially observed env…
Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination
Keane Lucas, Ross E. Allen
Cooperative artificial intelligence with human or superhuman proficiency in collaborative tasks stands at the frontier of machine learning research. Prior work has tended to evalua…
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
Ho Chit Siu, Jaime D. Pena, Edenna Chen +5
Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machi…
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning
Sheng Li, Yutai Zhou, Ross Allen +1
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication wh…
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Sheng Li, Jayesh K. Gupta, Peter Morales +2
Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of…
Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning
Ross E. Allen, Jayesh K. Gupta, Jaime Pena +3
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a poli…