RL²: Fast reinforcement learning via slow reinforcement learning

Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks

arXiv:2605. 22305v2 Announce Type: replace Abstract: We analytically solve the Mountain Car problem, a canonical benchmark in RL, and derive an optimal control solution, closing a gap after 36 years.

Dota 2 with large scale deep reinforcement learning

Heavy-Ball Q-Learning with Residual Weighting Correction

This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies conditions under which the method is theoretically guaranteed to converge faster than standard Q-learning.

Heavy-Ball Q-Learning with Residual Weighting Correction

arXiv:2606. 27112v1 Announce Type: cross Abstract: This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence.

From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial

arXiv:2601. 08662v3 Announce Type: replace Abstract: This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations.

Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach

arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.

Gotta Learn Fast: A new benchmark for generalization in RL

Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents

arXiv:2609.36393v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) techniques focus on maximizing expected cumulative reward, where each action assumes to take a constant unit...

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

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 s...

Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability

arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control

arXiv:2608. 07870v1 Announce Type: new Abstract: Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly.

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

RLLBC-Lib is an educational code library designed to lower the entry barrier for students learning reinforcement learning (RL) in the context of learning-based control. It offers a comprehensive collection of tabular RL methods to reinforce theoretical foundations, followed by a deep RL library that mirrors the same design principles to highlight parallels between simple and state‑of‑the‑art approaches. The library also includes implementations that illustrate core RL principles, contrast RL with other learning‑based control methods, and serve as a foundation for programming assignments with automated grading.