AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.
Dealing with sparse rewards in reinforcement learning
3 Pith papers cite this work. Polarity classification is still indexing.
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Framework using parameterized Signal Temporal Logic specifications to shape rewards for PPO-based RL, yielding tighter velocity tracking and more stable training than hand-crafted rewards on Barkour quadruped in MuJoCo simulation.
Mesh-RL applies finite-element-inspired domain decomposition with overlapping subgrids to accelerate temporal-difference learning across distant states in grid-world environments.
citing papers explorer
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Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?
AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.
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Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications
Framework using parameterized Signal Temporal Logic specifications to shape rewards for PPO-based RL, yielding tighter velocity tracking and more stable training than hand-crafted rewards on Barkour quadruped in MuJoCo simulation.
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Mesh-RL: Coupled subgrid reinforcement learning
Mesh-RL applies finite-element-inspired domain decomposition with overlapping subgrids to accelerate temporal-difference learning across distant states in grid-world environments.