16 citations · 21 across the 3 of their papers we have counts for
5 papers
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…