Complexity Reduction Study Based on RD Costs Approximation for VVC Intra Partitioning

A new framework utilizing reinforcement learning (RL) has been introduced to optimize flip angle schedules in Magnetic Resonance Fingerprinting (MRF), enhancing the distinguishability of fingerprints across the parameter space. This RL approach automates the selection of parameters, potentially reducing acquisition times in MRF processes.

A new methodology has been introduced to enhance the generalizability of Reinforcement Learning (RL) agents by predicting their performance across different environments based on the internal weights of their neural networks. This approach modifies the Proximal Policy Optimization (PPO) loss function, resulting in agents that demonstrate improved adaptability compared to traditional models.

A new model-based algorithm named BLINQ has been introduced, which learns the Whittle indices of an indexable, communicating, and unichain Markov Decision Process (MDP). This approach builds an empirical estimate of the MDP and computes its Whittle indices using an enhanced version of an existing algorithm, demonstrating convergence and computational efficiency.

Recent research has critically evaluated the effectiveness of Reinforcement Learning with Verifiable Rewards (RLVR) in enhancing the reasoning capabilities of large language models (LLMs). The study found that while RLVR-trained models perform better than their base counterparts on certain tasks, they do not exhibit fundamentally new reasoning patterns, particularly at larger evaluation metrics like pass@k.

A recent study has framed the self-healing process of material systems as a Reinforcement Learning (RL) problem within a Markov Decision Process (MDP), demonstrating that RL agents can autonomously derive optimal policies for maintaining structural integrity while managing resource consumption. The research highlighted the superior performance of continuous-action agents, particularly the TD3 agent, in achieving near-complete material recovery compared to traditional heuristic methods.

A novel multi-level reinforcement learning (RL) method has been developed, inspired by human decision-making processes that differentiate between various levels of performance. This approach aims to enhance learning by extracting multi-level information from experiences, contrasting with traditional RL that treats all experiences uniformly.

The introduction of Perceptual-Evidence Anchored Reinforced Learning (PEARL) marks a significant advancement in multimodal reasoning, addressing the limitations of traditional Reinforcement Learning with Verifiable Rewards (RLVR) in Vision-Language Models (VLMs). PEARL enhances reasoning by anchoring it to verified visual evidence, thus mitigating issues like visual hallucinations and reward hacking.

A recent study explores the enhancement of Reinforcement Learning (RL) in 3D visuospatial tasks through a human-informed curriculum design, aiming to improve the technology's effectiveness in complex problem domains. The research highlights the challenges faced by state-of-the-art RL methods, such as PPO and imitation learning, in mastering these tasks.