Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
Reinforcement learning for combinatorial optimization: A survey
Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …
Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue
Abstract Recent advances in Large Language Models (LLMs) have achieved remarkable
breakthroughs in understanding and responding to user intents. However, their performance …
breakthroughs in understanding and responding to user intents. However, their performance …
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
Difusco: Graph-based diffusion solvers for combinatorial optimization
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
Learning to dispatch for job shop scheduling via deep reinforcement learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
Solving mixed integer programs using neural networks
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …
developed with decades of research to solve large-scale MIP instances encountered in …
Pomo: Policy optimization with multiple optima for reinforcement learning
In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …
Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …
solve various sequential decision-making problems. These algorithms, however, have faced …