DR.Q debiases model-based representations for Q-learning by maximizing mutual information between state-action and next-state representations and applying faded prioritized experience replay, achieving competitive or superior performance on continuous control benchmarks.
Bigger, regularized, categorical: High-capacity value functions are efficient multi-task learners.arXiv preprint arXiv:2505.23150, 2025
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FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
Flow matching critics outperform monolithic ones in RL by 2x performance and 5x sample efficiency via test-time error recovery through integration and multi-point velocity supervision that preserves feature plasticity.
Non-uniform replay helps most when replay volume is low; high-entropy sampling remains important, and a truncated geometric distribution delivers better sample efficiency with negligible overhead.
citing papers explorer
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Debiased Model-based Representations for Sample-efficient Continuous Control
DR.Q debiases model-based representations for Q-learning by maximizing mutual information between state-action and next-state representations and applying faded prioritized experience replay, achieving competitive or superior performance on continuous control benchmarks.
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FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
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What Does Flow Matching Bring To TD Learning?
Flow matching critics outperform monolithic ones in RL by 2x performance and 5x sample efficiency via test-time error recovery through integration and multi-point velocity supervision that preserves feature plasticity.
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When Does Non-Uniform Replay Matter in Reinforcement Learning?
Non-uniform replay helps most when replay volume is low; high-entropy sampling remains important, and a truncated geometric distribution delivers better sample efficiency with negligible overhead.