2 papers
cs.LG2025
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning
Junseok Park, Hyeonseo Yang, Min Whoo Lee +3
Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biolog…
cs.MA2025
Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning
Min Whoo Lee, Kibeom Kim, Soo Wung Shin +2
Applying multi-agent reinforcement learning methods to realistic settings is challenging as it may require the agents to quickly adapt to unexpected situations that are rarely or n…