The paper discusses the challenges of optimizing the cost function parameters of Model Predictive Control (MPC) for autonomous vehicles. The authors propose using Reinforcement Learning (RL) to learn optimal parameter…
Continue readingThis study presents a novel attempt to replicate human neuromechanical experiments in a virtual environment using a digital twin of the human model. The model was enhanced using a human…
Continue readingThis research presents the first critic-actor algorithm with function approximation and finite-time analysis, used in the long-run average reward setting. The algorithm is designed to solve reinforcement learning problems by…
Continue readingThe paper focuses on aligning language models with human preferences for real-world applications. It discusses the drawbacks of reinforcement learning (RL) and direct preference optimization (DPO) in achieving this goal….
Continue readingThe article introduces PetriRL, a new framework for resolving job shop scheduling problems (JSSP) by integrating Petri nets and reinforcement learning. The approach improves the explainability of neural networks and…
Continue readingThe article introduces RadDQN, a deep Q-learning architecture aimed at achieving maximum radiation protection in the nuclear industry. The system operates on a radiation-aware reward function and employs unique exploration…
Continue readingThis academic article is about the Factorized Multi-Agent MiniMax Q-Learning (FM3Q), a new framework for two-team zero-sum Markov games. The authors identify inefficiencies in existing methods and propose the individual-global-minimax…
Continue readingThe study focuses on Reinforcement Learning from Human Feedback (RLHF) and how it can be optimized. Traditionally, RLHF involves generating completions from a language model in response to a query…
Continue readingThis article explores the use of shielding in reinforcement learning (RL) for continuous environments. Classical shielding approaches have limitations, making them hard to use in complex environments. The authors extend…
Continue readingThe article introduces SLIM, a multi-critic learning approach for skill discovery, particularly focusing on robotic manipulation. It addresses the limitations of mutual information maximization in producing useful manipulation behaviors. The…
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