New rl-triton library speeds up RL credit assignment with unified GPU kernels

Researchers have developed rl-triton, an open-source library featuring high-performance GPU kernels for reinforcement learning credit assignment. This library unifies seven distinct RL estimation algorithms, including GAE and V-Trace, under a single associative scan framework. Implemented in Triton, the kernels solve these algorithms as linear recurrences in logarithmic parallel steps, achieving significant speedups over existing baselines. AI

IMPACT Accelerates research and development in reinforcement learning by providing optimized GPU kernels for credit assignment.

RANK_REASON The item is a research paper detailing a new open-source library for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• discounted returns
• Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules
• episodic prefix sums
• Generalized Advantage Estimation
• High Bandwidth Memory
• Hugging Face
• Retrace($\lambda$)
• rl-triton
• TD($\lambda$)
• Triton
• V-Trace

• paper
• infra

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