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cs.LG2025
TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design
Geonwoo Cho, Jaegyun Im, Jihwan Lee +3
Generalizing deep reinforcement learning agents to unseen environments remains a significant challenge. One promising solution is Unsupervised Environment Design (UED), a co-evolut…
cs.LG2024
DIAR: Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation
Jaehyun Park, Yunho Kim, Sejin Kim +2
We propose a novel offline reinforcement learning (offline RL) approach, introducing the Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation (DIAR) framework. We a…