reinforcement learning

Reinforcement learning based control of packed bed reactors for chemical looping combustion

Deep reinforcement learning for multi-Objective latency minimization in 5G heterogeneous vehicular networks

Multiple targets tracking with unmanned aerial vehicle swarm based on multi-agent deep reinforcement learning framework with safety network

Advancing AI‑powered hybrid plants: Reinforcement learning control of small‑scale solar Brayton turbines via surrogate modelling and fuel‑air ratio insensitivity discovery

Supplementary Data for: Mechanism-informed graph reinforcement learning for resource-constrained maintenance scheduling in IFMIF-DONES

This dataset contains the supplementary material supporting the manuscript "Mechanism-informed graph reinforcement learning for resource-constrained maintenance scheduling in IFMIF-DONES". It includes the Master Sequence of Operations (best-observed SOP) generated by the GCN-TD3 optimizer to achieve the 71.43-hour make...

自适应图神经网络的结构优化

This paper presents a novel approach to self-adaptive graph neural networks, aiming to automatically optimize the network structure based on data distribution. Traditional graph neural networks often require manual design of the network architecture, limiting their adaptability. Our proposed method employs an adaptive...

Scalable Modular Architectures for Naturalistic Behavior and Cognitive Mapping in Biological and Artificial Intelligence

Introduction Biological intelligence is far more adaptive, autonomous and efficient than current artificial intelligence, despite recent advances. Neuroethological comparisons and computational simulations show that nervous systems across phyla share a conserved modular organization for information flow from sensation...

Nonlinear Constraints and Adaptive Variable Weight Optimization

This paper explores a novel algorithm for non-linear constraint satisfaction, focusing on adaptive variable weight optimization. Traditional approaches often struggle with variables that change over time, leading to suboptimal solutions. We introduce a dynamic variable weighting mechanism that automatically adjusts wei...

Reviewer #1 (Public review): Correctness is its own reward: bootstrapping error signals in self-guided reinforcement learning

Information-Theoretic Validation of Reinforcement Learning Policies

Reinforcement learning (RL) relies heavily on reward signals to guide policy learning. However, conventional metrics like cumulative reward and average return frequently fail to capture the nuances of policy performance, particularly in complex environments. This work proposes a novel approach to validating RL policies...

Multi-Objective Reinforcement Learning with Pareto Dominance-Based Exploration

Multi-objective reinforcement learning (MORL) presents significant challenges due to the difficulty in balancing multiple, often conflicting, objectives. Traditional exploration methods often fail to adequately explore the complex multi-objective action space, leading to suboptimal solutions. This paper proposes a nove...

Dynamic Neuron Topology Mapping and Adaptive Neuron Connection Generation

This paper proposes a novel framework for dynamically mapping neuron topologies and generating adaptive neuron connections within neural networks. The core idea is to integrate topological network modeling with reinforcement learning to create a system capable of learning and adapting to complex neural network topologi...