#d4rl-benchmark News & Analysis

2 articles tagged with #d4rl-benchmark. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Jun 106/10

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Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning

Researchers introduce Bootstrapped Flow Q-Learning (BFQ), a new offline reinforcement learning method that achieves single-step action generation without multi-step denoising, improving computational efficiency and performance over existing diffusion-based approaches. The framework eliminates auxiliary networks and distillation procedures while maintaining high expressiveness, demonstrated through D4RL benchmark evaluations.

AINeutralarXiv – CS AI · Mar 55/10

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IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning

Researchers propose Imaginary Planning Distillation (IPD), a novel framework that enhances offline reinforcement learning by incorporating planning into sequential policy models. IPD uses world models and Model Predictive Control to generate optimal rollouts, training Transformer-based policies that significantly outperform existing methods on D4RL benchmarks.