7 papers
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…
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
Andrea Tagliabue, Dong-Ki Kim, Michael Everett +1
We propose a demonstration-efficient strategy to compress a computationally expensive Model Predictive Controller (MPC) into a more computationally efficient representation based o…
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation
Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How
In a multirobot system, a number of cyber-physical attacks (e.g., communication hijack, observation perturbations) can challenge the robustness of agents. This robustness issue wor…
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Dong-Ki Kim, Miao Liu, Matthew Riemer +6
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each…
Policy Distillation and Value Matching in Multiagent Reinforcement Learning
Samir Wadhwania, Dong-Ki Kim, Shayegan Omidshafiei +1
Multiagent reinforcement learning algorithms (MARL) have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works h…
Learning Hierarchical Teaching Policies for Cooperative Agents
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei +7
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teamma…