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cs.LG2026
In-Distribution Imagination for Model-Based Offline Reinforcement Learning
Mintae Kim, Koushil Sreenath
Model-based offline reinforcement learning (MBORL) improves sample efficiency through model-generated trajectories. However, accumulative model error can drive imagined trajectorie…
cs.LG2026
Robust Adversarial Policy Optimization Under Dynamics Uncertainty
Mintae Kim, Koushil Sreenath
Reinforcement learning (RL) policies often fail under dynamics that differ from training, a gap not fully addressed by domain randomization or existing adversarial RL methods. Dist…
cs.LG2026
WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
Mintae Kim, Koushil Sreenath
Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task. Offline-to-onl…