reinforcement learning

Load frequency control framework of renewable integrated power systems in multi scenario disturbance conditions with the proximal policy optimization

Abstract The rapid adoption of renewable energy sources has radically changed the dynamic qualities of the modern power system, making load frequency control (LFC) problem worse due to unforeseeable generation, reduced system inertia, and the constant changes in disturbances. This paper presents a control framework bas...

Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language

Language models are typically trained to predict the next token in a sequence. Here, we explore an alternative predictive principle from reinforcement learning: Successor Representations (SRs), which model the expected discounted distribution of future states rather than the immediate next state. We transfer this frame...

LUPittore: software che impara da solo a dipingere come un umano, prima versione, verrà accelerato l'apprendimento

# LUPittore[cite: 2] ## Description[cite: 2] LUPittore is an autonomous, standalone Python desktop application for Windows 10 and Windows 11 combining raster canvas manipulation, multilayer digital compositing, procedural brush engines, and autonomous stroke-based rendering (SBR)[cite: 2]. The software is implemented e...

LUPittore: software che impara da solo a dipingere come un umano, prima versione, verrà accelerato l'apprendimento

# LUPittore[cite: 2] ## Description[cite: 2] LUPittore is an autonomous, standalone Python desktop application for Windows 10 and Windows 11 combining raster canvas manipulation, multilayer digital compositing, procedural brush engines, and autonomous stroke-based rendering (SBR)[cite: 2]. The software is implemented e...

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...