103 citations · 355 across the 24 of their papers we have counts for
4 papers · 1 filter
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria
Dong-Ki Kim, Matthew Riemer, Miao Liu +3
Multiagent learning settings are inherently more difficult than single-agent learning because each agent interacts with other simultaneously learning agents in a shared environment…
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
Dong-Ki Kim, Matthew Riemer, Miao Liu +5
The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behavio…
Hierarchical Reinforcement Learning with AI Planning Models
Junkyu Lee, Michael Katz, Don Joven Agravante +4
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrat…
Local Explanations for Reinforcement Learning
Ronny Luss, Amit Dhurandhar, Miao Liu
Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood b…