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RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control

Paper recorded by Signals 4 on 2026-09-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.AI · 人工智能 · first seen 2026-09-17

Abstract

Reinforcement learning (RL) is an exciting concept as well as a remarkable success story worth sharing. However, RL builds on rather complex interactions between different objects that play out over several cycles. Such dynamics are often best explained with an easily accessible implementation. We present RLLBC-Lib, a carefully crafted code library with the goal of lowering the entry barrier for s

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#34 most recent of 300 cs.AI papers we have recorded · ↑ newer: Double descent is the principle of least action · ↓ older: Social Laws for Multi-agent Coordination in Stochastic Environments
Cite this page: RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control: the #34 most recent of 300 cs.AI papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/rllbc-lib-an-educational-code-library-for-reinforcement-learning-and-learning-ba.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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