9 citations · 9 across the 3 of their papers we have counts for
5 papers
Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL
Dongsu Lee, Sooraj Sathish, Joonkyung Kim +2
We argue that cooperative multi-agent reinforcement learning (MARL) should treat test-time coordination as part of the problem definition rather than a deployment artifact. Our foc…
Test-time Multi-agent Coordination by Decomposed Value Gradient Flow
Dongsu Lee, Haoran Xu, Amy Zhang
Offline multi-agent reinforcement learning (MARL) faces a persistent trade-off. Expressive generative policies can represent multi-modal coordination in the data, but cannot distin…
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
Dongsu Lee, Minhae Kwon
The goal of offline reinforcement learning (RL) is to extract a high-performance policy from the fixed datasets, minimizing performance degradation due to out-of-distribution (OOD)…
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
Dongsu Lee, Minhae Kwon
Understanding cognitive processes in multi-agent interactions is a primary goal in cognitive science. It can guide the direction of artificial intelligence (AI) research toward soc…
AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset
Dongsu Lee, Chanin Eom, Minhae Kwon
Offline reinforcement learning has emerged as a promising technology by enhancing its practicality through the use of pre-collected large datasets. Despite its practical benefits,…