reinforcement learning

Title: Adaptive Constraint Network Learning (ACNL)

Adaptive Constraint Network Learning (ACNL) presents a novel approach to constraint programming, addressing the limitations of static constraint definition. Traditional constraint programming methods often require manual configuration of constraints, which can be time-consuming and limit the flexibility of the problem....

基于多智能体协作的分布式算法设计方法

This paper presents a novel approach to distributed algorithm design leveraging the power of multi-agent collaboration. The core idea is to automate the design and optimization process by training a population of intelligent agents through techniques such as evolutionary algorithms or reinforcement learning. These agen...

Title: Dynamic Topology for Parallel Computation

This paper introduces Dynamic Topology for Parallel Computation (DTPC), a novel framework for dynamically adjusting the topology of parallel computation graphs. Traditional parallel programming often relies on manual tuning, which can be time-consuming and suboptimal. DTPC leverages reinforcement learning to automatica...

Probability Space Learning Adaptive Time Scale Optimization

This paper introduces a novel probabilistic space learning framework for adaptive time scale optimization, designed to improve model generalization and efficiency. Traditional reinforcement learning and deep learning often employ fixed time scales, while our approach dynamically adjusts these scales based on data distr...

Neural Collective Behavior Modeling and Reinforcement Learning

This paper explores the application of mathematical modeling and reinforcement learning to the study of neural network collective behavior. Traditional neural network optimization often relies on manual parameter tuning, limiting the model's adaptability. We propose a novel framework that integrates these approaches, s...

Dynamic Information Entropy Optimization: A Reinforcement Learning Approach

This paper introduces a novel reinforcement learning algorithm, termed "Dynamic Information Entropy Optimization" (DIEO), that leverages dynamic information entropy to guide the learning process. Traditional reinforcement learning often relies on static reward functions, limiting the agent's ability to adapt to complex...

Title: Dynamically Reconfigurable Chaos Theory

This paper explores the concept of dynamically reconfigurable chaos theory, a novel approach to modeling and controlling chaotic systems by employing reinforcement learning to actively adjust parameter values in real-time. Traditional chaos theory often relies on static parameter settings, limiting its adaptability to...

Multi-Agent Reinforcement Learning for Traffic Control with Dynamic Route Optimization

This paper presents a novel approach to traffic control utilizing Multi-Agent Reinforcement Learning (MARL) for dynamic route optimization within a traffic network. Traditional traffic management systems often struggle to adapt effectively to fluctuating traffic conditions and emergent congestion. This research propose...

Simulation data for "Internal-state criticality in Bayesian–inverse-Bayesian inference"

# Data archive — Internal-state criticality in Bayesian–inverse-Bayesian inference **Paper.** *Internal-state criticality in Bayesian–inverse-Bayesian inference*, K. Sasai and Y.-P. Gunji (Physical Review Research, submitted). **Source repository.** **DOI.** `10.5281/zenodo.20533918`. --- ## Contents This deposit conta...

Supplementary Information for the Paper "Physical Interaction Effects Reveal the Necessary

We conduct a systematic two-year experimental study across 22 contact-rich insertion tasks to determine whether the failure of learning-based robot control on tight-tolerance assembly reflects a fundamental structural limitation. Evaluating five levels of model knowledge across 693,000 real-hardware trials, we show tha...

QRL-FP: a quantum-inspired reinforcement learning framework for multi-objective system-on-chip floorplanning

Seq2Seq2Seq: lossless data compression via discrete latent transformers and reinforcement learning

Abstract Efficient lossless compression is essential for reducing storage and transmission requirements while preserving data exactly. Traditional dictionary-based and statistical compressors can be limited in their ability to exploit complex, long-range dependencies. In this paper, we propose a lossless compression fr...