Search Results - subject:"Deep Reinforcement Learning"

Dynamic personalization with multiple customer signals : multi-response state representation in reinforcement learning

Ma, Liangzong; Huang, Ta-Wei; Ascarza, Eva; Israeli, Ayelet - 2025

Persistent link: https://www.econbiz.de/10015197090

A deep reinforcement learning hyperheuristic for the covering tour problem with varying coverage

Torabi, Parisa; Hemmati, Ahmad; Oleynik, Anna; Alendal, … - In: Computers & operations research : an international journal 174 (2025), pp. 1-11

Persistent link: https://www.econbiz.de/10015330145

Transfer reinforcement learning for pricing, driver repositioning and customer admission in ride-hailing networks

De Munck, Thomas; Tancrez, Jean-Sébastien; Chevalier, … - 2025

Persistent link: https://www.econbiz.de/10015330434

Deep reinforcement learning in non-Markov market-making

Lalor, Luca; Sviščuk, Anatolij - 2025

We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically …

Persistent link: https://www.econbiz.de/10015358963

High-dimensional multi-period portfolio allocation using deep reinforcement learning

Jiang, Yifu; Olmo, Jose; Atwi, Majed - In: International review of economics & finance : IREF 98 (2025), pp. 1-18

Persistent link: https://www.econbiz.de/10015333037

Deep Reinforcement Learning in agent-based model AgriPoliS to simulate strategic land market interactions

Dong, Changxing; Njiru, Ruth Dionisia Gicuku; Appel, … - In: Electronic Communications of the EASST 83 (2025), pp. 1-18

AgriPoliS (Agricultural Policy Simulator) is an Agent-Based Model for simulating the dynamic evolution of agricultural regions with a particular emphasis on the structural transformations influenced by economic, ecological, and societal factors. This study introduces a Reinforcement Learning...

Persistent link: https://www.econbiz.de/10015210972

Can deep reinforcement learning beat 1/N

Kruthof, Garvin; Müller, Sebastian - In: Finance research letters 75 (2025), pp. 1-13

Persistent link: https://www.econbiz.de/10015408267

Quantile preferences in portfolio choice: A Q-DRL approach to dynamic diversification

Sarkany, Attila; Janásek, Lukáš; Baruník, Jozef - 2024

a Quantile Deep Reinforcement Learning (Q-DRL) algorithm. The research will provide a new level of understanding the …

Persistent link: https://www.econbiz.de/10014577259

Quantile preferences in portfolio choice : a Q-DRL approach to dynamic diversification

Sarkany, Attila; Janásek, Lukáš; Baruník, Jozef - 2024

a Quantile Deep Reinforcement Learning (Q-DRL) algorithm. The research will provide a new level of understanding the …

Persistent link: https://www.econbiz.de/10014532001

Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems

Kaven, Lea; Huke, Philipp; Göppert, Amon; Schmitt, … - In: Journal of Intelligent Manufacturing 35 (2024) 8, pp. 3917-3936

scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal …

Persistent link: https://www.econbiz.de/10015371298