arXiv:2607. 14001v1 Announce Type: new Abstract: We suggest using the Lyapunov characteristic exponent (LCE) as a dense reward signal for the reinforcement learning problem of stabilizing the inverted pendulum with vertical motion.
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.
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.
arXiv:2608. 02433v1 Announce Type: new Abstract: Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback.
arXiv:2602. 04132v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has achieved remarkable success in solving complex sequential decision-making problems.
The paper introduces a framework for combining large language models (LLMs) with reinforcement learning (RL) by treating the LLM as a planner and the RL agent as a controller. It formalizes this hybrid setup as a Goal-Augmented Markov Decision Process and proves that using the LLM’s per‑state progress score as a bounded potential function preserves the optimal policy set, even if the LLM scores are inaccurate. The authors validate their theoretical result with numerical experiments on a small MDP, testing four potential configurations, including an adversarial case with a potential scaled twenty times the base reward.
arXiv:2607. 18365v1 Announce Type: cross Abstract: Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain.
The paper discusses how reinforcement learning theory relies on probability theory via Markov chains and highlights a deep link between probability theory and potential theory. It reviews this connection and examines how a potential-theoretic perspective can be applied to core RL representations and algorithms under a fixed‑policy assumption, suggesting possible gains in sample efficiency and formal constraints. The authors also note that relaxing the fixed‑policy assumption allows the linear potential theory framework to extend naturally to nonlinear cases.
arXiv:2410. 11894v3 Announce Type: replace-cross Abstract: Dynamical systems form the foundation of scientific discovery, traditionally modeled with predefined state variables such as the angle and angular velocity, and differential equations such as the equation of motion for a single pendulum.
arXiv:2606. 10835v1 Announce Type: cross Abstract: Periodic hard target updates are among the most common stabilization devices in modern deep Q-learning.
We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge.
arXiv:2606. 02645v1 Announce Type: cross Abstract: Periodic target updates in Q-learning and soft target updates in actor-critic methods are empirically well established stabilization mechanisms, but their precise theoretical explanation is still incomplete.