A systematic study on reinforcement learning based applications
We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Beyond games: a systematic review of neural Monte Carlo tree search applications
The advent of AlphaGo and its successors marked the beginning of a new paradigm in
playing games using artificial intelligence. This was achieved by combining Monte Carlo …
playing games using artificial intelligence. This was achieved by combining Monte Carlo …
[HTML][HTML] Discovering Lin-Kernighan-Helsgaun heuristic for routing optimization using self-supervised reinforcement learning
Q Wang, C Zhang, C Tang - Journal of King Saud University-Computer and …, 2023 - Elsevier
Vehicle routing optimization is a crucial responsibility of transportation service providers,
which can significantly reduce operating expenses and improve client satisfaction. Learning …
which can significantly reduce operating expenses and improve client satisfaction. Learning …
VARL: a variational autoencoder-based reinforcement learning Framework for vehicle routing problems
Q Wang - Applied Intelligence, 2022 - Springer
The vehicle routing problem as a classic NP-hard problem could be optimized by path
choices due to its practical application value. This study proposes a novel variational …
choices due to its practical application value. This study proposes a novel variational …
[HTML][HTML] Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks
Abstract Missions involving Unmanned Aerial Vehicle usually consist of reaching a set of
regions, performing some actions in each region, and returning to a determined depot after …
regions, performing some actions in each region, and returning to a determined depot after …
[HTML][HTML] Generative inverse reinforcement learning for learning 2-opt heuristics without extrinsic rewards in routing problems
Deep reinforcement learning (DRL) has shown promise in solving challenging combinatorial
optimization (CO) problems, such as the traveling salesman problem (TSP) and vehicle …
optimization (CO) problems, such as the traveling salesman problem (TSP) and vehicle …
Efficient graph neural architecture search using Monte Carlo Tree search and prediction network
TJ Deng, J Wu - Expert Systems with Applications, 2023 - Elsevier
Abstract Graph Neural Networks (GNNs) have emerged recently as a powerful way of
dealing with non-Euclidean data on graphs, such as social networks and citation networks …
dealing with non-Euclidean data on graphs, such as social networks and citation networks …
Routing optimization with Monte Carlo Tree Search-based multi-agent reinforcement learning
Q Wang, Y Hao - Applied Intelligence, 2023 - Springer
Vehicle routing (VRP) and traveling salesman problems (TSP) are classical and interesting
NP-hard routing combinatorial optimization (CO) with practical significance. While moving …
NP-hard routing combinatorial optimization (CO) with practical significance. While moving …
Dynamic optimization of intersatellite link assignment based on reinforcement learning
W Ren, J Zhu, H Qi, L Cong… - International Journal of …, 2022 - journals.sagepub.com
Intersatellite links can reduce the dependence of satellite communication systems on ground
networks, reduce the number of ground gateways, and reduce the complexity and …
networks, reduce the number of ground gateways, and reduce the complexity and …
AI Advancements: Comparison of Innovative Techniques
In recent years, artificial intelligence (AI) has seen remarkable advancements, stretching the
limits of what is possible and opening up new frontiers. This comparative review investigates …
limits of what is possible and opening up new frontiers. This comparative review investigates …