reinforcement learning

DPO vs. RLHF: An Empirical Comparison of Alignment Techniques for Large Language Models

This work presents a reproducible empirical comparison of Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF) for Large Language Model alignment. Experiments are conducted on GPT-2 (124M parameters) using the Anthropic HH-RLHF dataset. The study evaluates alignment quality, reward...

Artificial intelligence in microbial biotechnology for food security: current state and challenges

Challenges that beset the current landscape still include overall adoption, difficult-to-understand models or algorithm interpretability, quality input of training data, good ethical practices, high computational cost associated with complex structural simulations, and the need for standardized processes.

Smart hydropower generation and pressure management in water distribution networks using a novel in-pipe turbine

In-pipe turbines have emerged as a promising solution for harnessing surplus energy in water transmission networks for distributed power generation. While much of the previous research has focused on optimizing the turbine itself, practical implementation presents additional challenges. To address these challenges, thi...

基于动态拓扑记忆网络的神经符号推理

This paper proposes a novel approach to neural-symbolic reasoning by introducing a Dynamic Topological Memory Network (DTMN). The core idea is to construct a memory network capable of dynamically adjusting its internal topology to mirror the complexity and relationships within the input data. This addresses a key limit...

Learning to Replay: A meta-reinforcement learning approach to experience replay in continual learning

基于多智能体强化学习的分布式资源调度优化

This paper investigates the optimization of distributed resource scheduling using a multi-agent reinforcement learning (MARL) framework. Traditional resource scheduling methods often rely on static rules or centralized control, which can be inflexible and inefficient in dynamic environments. This research proposes a no...

##基于深度强化学习的智能交通信号控制

This paper investigates the application of Deep Reinforcement Learning (DRL) for intelligent traffic signal control. Traditional traffic signal control methods often rely on pre-defined rules or simple optimization algorithms, which may not effectively adapt to dynamic traffic conditions. DRL offers a promising approac...

基于异质性网络的自适应强化学习

This paper introduces a novel framework for reinforcement learning, termed "Adaptive Heterogeneous Networks" (AHN), designed to dynamically adjust learning strategies based on the environment's dynamic characteristics. Traditional reinforcement learning often relies on static policies, limiting adaptability. AHN levera...

基于自适应的自组织神经网络结构优化

This paper presents a novel algorithm for optimizing the structure of self-organizing neural networks (SONNs) through dynamic adjustment of connection strengths. Traditional SONNs often rely on manually defined layer configurations, which can be suboptimal and require significant tuning. Our proposed algorithm, termed...

基于动态拓扑的非线性强化学习算法

This paper introduces a novel dynamic topology-based non-linear reinforcement learning (RL) algorithm designed to enhance learning efficiency and robustness. Traditional reinforcement learning methods often rely on static strategies, limiting adaptability and vulnerability to environmental changes. Our algorithm dynami...

基于多模态上下文的动态程序优化

This paper introduces a novel approach to dynamic program optimization leveraging multi-modal contextual information. The core idea is to combine code execution data, static code analysis, and natural language descriptions to enable adaptive and dynamic optimization strategies. We propose a deep learning-based framewor...

基于自适应的非线性几何模型的预测

This paper introduces a novel prediction framework based on adaptive non-linear geometry models. Traditional predictive models often rely on fixed parameters, limiting their accuracy and efficiency. Our approach dynamically adjusts model parameters to capture the complex, non-linear relationships within the system bein...