Model-Based Learning of Whittle indices

A recent study has been conducted on the Versatile Video Codec (VVC) intra partitioning, focusing on reducing complexity in the Rate-Distortion Optimization (RDO) process. The research proposes two machine learning techniques that utilize the Rate-Distortion costs of neighboring blocks, aiming to enhance the efficiency of the exhaustive search typically required in video coding.

Recent advancements in Actor Critic algorithms have led to the proposal of a new framework that decouples the acting policy from the critic's policy, allowing for more aggressive updates to the critic while maintaining stability in the acting policy. This approach aims to enhance the learning process in decision-making problems by balancing greedification with stability.

Recent advancements in Contrastive Local Learning Networks (CLLNs) have demonstrated their potential for reinforcement learning (RL) applications, particularly in energy-limited environments. This study successfully applied Q-learning techniques to simulated CLLNs, showcasing their robustness and low power consumption compared to traditional digital systems.

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.

The introduction of the Non-stationary and Varying-discounting Markov Decision Processes (NVMDP) framework addresses the limitations faced by traditional stationary Markov Decision Processes (MDPs) in non-stationary environments. This framework allows for varying discount rates over time and transitions, making it applicable to both finite and infinite-horizon tasks.

AVA-VLA is a newly proposed framework aimed at enhancing Vision-Language-Action (VLA) models by integrating Active Visual Attention (AVA) to improve visual processing in dynamic decision-making contexts. This approach addresses the limitations of traditional VLA models that operate independently at each timestep, which can hinder effective contextual understanding in sequential tasks.

A new refinement in temporal-difference learning has been proposed, emphasizing first-order Bellman consistency. This approach trains the learned value function to align with both the Bellman targets and their derivatives, enhancing the stability and convergence of reinforcement learning algorithms like Q-learning and actor-critic methods.

A novel Q-learning framework has been proposed for time-critical data aggregation scheduling in Internet of Things (IoT) networks, aiming to reduce latency in applications such as smart cities and industrial automation. This approach integrates aggregation tree construction and scheduling into a unified model, enhancing efficiency and scalability.