75 citations · 244 across the 14 of their papers we have counts for
7 papers · 1 filter
Open Problems in Cooperative AI
Allan Dafoe, Edward Hughes, Yoram Bachrach +5
Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such…
Negotiating Team Formation Using Deep Reinforcement Learning
Yoram Bachrach, Richard Everett, Edward Hughes +6
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a bin…
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Raphael Köster, Kevin R. McKee, Richard Everett +7
Game theoretic views of convention generally rest on notions of common knowledge and hyper-rational models of individual behavior. However, decades of work in behavioral economics…
Learning to Incentivize Other Learning Agents
Jiachen Yang, Ang Li, Mehrdad Farajtabar +3
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single…
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Edward Hughes, Thomas W. Anthony, Tom Eccles +3
Zero-sum games have long guided artificial intelligence research, since they possess both a rich strategy space of best-responses and a clear evaluation metric. What's more, compet…
Social diversity and social preferences in mixed-motive reinforcement learning
Kevin R. McKee, Ian Gemp, Brian McWilliams +3
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinfor…