Conservative Offline RL with Uncertainty-Aware Policy Improvement
We study a conservative offline reinforcement learning algorithm with uncertainty-aware policy updates, evaluate it on standard benchmarks, and analyze failure modes.
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problemFeb 9, 2026Open Questionrl · vision
Research problem 36: Planning systems
This study explores automated research pipelines with rigorous evaluation, detailed ablations, and transparent artifacts. This study explores automated research pipelines with rigo…
problemFeb 1, 2026Open Questiongraph · safety
Research problem 28: Theory systems
This study explores automated research pipelines with rigorous evaluation, detailed ablations, and transparent artifacts. This study explores automated research pipelines with rigo…