12 citations · 25 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
Context-Specific Representation Abstraction for Deep Option Learning
Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer +3
Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.…
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…
FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize
Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How
This paper investigates reinforcement learning with constraints, which are indispensable in safety-critical environments. To drive the constraint violation monotonically decrease,…
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…