reinforcement learning

Dynamic Neural Network Topology (DNTN)

This paper introduces the Dynamic Neural Network Topology (DNTN), a novel neural network architecture designed to overcome the limitations of static, connection-based networks. The core claim of this work is that by dynamically adjusting the physical connection strengths and topology of neurons in real-time, adaptive l...

Automated Theorem Proving with Neural Network Guidance

Automated theorem proving (ATP) aims to develop systems capable of mechanically proving mathematical theorems. Despite significant advancements, ATP systems often struggle with complex reasoning tasks, largely due to the inherent difficulty in representing and executing logical deduction rules. This work proposes a nov...

Bringing Reinforcement Learning to Multi-Period Financial Planning: A Bridge Between Learning-Enabled and Stochastic Optimization

This survey traces the mathematical evolution of multi-period financial planning over the past several decades, focusing on selected key methods ranging from classical stochastic optimization to learning-enabled decision systems.

AXIS Project: Proactive Biological Homeostasis via the ERSA Algorithm

Abstract: Biological organisms are open systems operating under rigorous thermodynamic constraints, where homeostatic maintenance requires a constant expenditure of energy to suppress entropic decay. Conventional medical interventions are primarily reactive, acting only after homeostatic collapse exceeds critical thres...

Tribological Performance of S-Glass and Graphene Reinforced Al6061 Composites: Experimental and Machine Learning Perspectives

PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing

Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential decisions made by an RL algorithm, while optimized to maximize overall pop...

Analytical coarse grained potential parameterization by reinforcement learning for anisotropic cellulose

Purkinje Cells in Crus I and II Encode the Visual Stimulus and the Impending Choice as Monkeys Learn a Reinforcement-Based Visuomotor Association Task

Evidence implicates the cerebellum in motor learning and performance and relatively simple sensorimotor associations such as classical conditioning. However, little is known about how Purkinje cells encode more complex associations, such as arbitrary stimulus–response mappings. To address this, we recorded from Crus I/...

Deep reinforcement learning-based optimization of reconfigurable intelligent surfaces (RIS) for 6G multi-user connectivity in NLOS environments

Abstract The transition towards the sixth generation (6G) of wireless networks requires ultra-high data rates and seamless connectivity, even in frequency bands like mmWave and THz, which are highly susceptible to physical blockages. RIS have emerged as a key technology in mitigating NLOS limitations through programmab...

Hierarchical thermal management integrating LSTM prediction and DDPG reinforcement learning for cascade waste heat recovery in a methanol reforming PEMFC ship

Experimental Push-Off Test Dataset for Shear Transfer Capacity in GFRP-Reinforced Concrete Interfaces

This dataset compiles experimental push-off test results for evaluating the shear transfer capacity of concrete interfaces reinforced with Glass Fiber-Reinforced Polymer (GFRP) reinforcement. The database includes geometric, material, and reinforcement-related parameters used to characterize the tested specimens, inclu...

Experimental Push-Off Test Dataset for Shear Transfer Capacity in GFRP-Reinforced Concrete Interfaces

This dataset compiles experimental push-off test results for evaluating the shear transfer capacity of concrete interfaces reinforced with Glass Fiber-Reinforced Polymer (GFRP) reinforcement. The database includes geometric, material, and reinforcement-related parameters used to characterize the tested specimens, inclu...