홈 › Papers

Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy

2024-06-03 · Riqiang Gao, Florin C. Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen

In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as Reinforced Leaf Sequencer (RLS) in a multi-agent framework for leaf sequencing. The RLS model offers improvements to time-consuming iterative optimization steps via large-scale training and can control movement patterns through the design of reward mechanisms. We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. Our findings reveal that the proposed RLS model can achieve reduced fluence reconstruction errors, and potential faster convergence when integrated in an optimization planner. Additionally, RLS has shown promising results in a full artificial intelligence RTP pipeline. We hope this pioneer multi-agent RL leaf sequencer can foster future research on machine learning for RTP.

📄 PDF Abstract BibTeX arXiv:2406.01853

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningMulti-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Methods 이 논문이 사용한 방법론

+ ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881||How do I resolve a dispute on Expedia? How do I resolve a dispute on Expedia contact their support at + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056. Provide booking details and explain the issue…

An Optimization Framework for Task Sequencing in Curriculum Learning

2019-01-31 · Francesco Foglino, Christiano Coletto Christakou, Matteo Leonetti

Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and ped…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning

2022-03-26 · Mohamed Akrout, Amal Feriani, Bob McLeod

We study the benefits of reinforcement learning (RL) environments based on agent-based models (ABM). While ABMs are known to offer microfoundational simulations at the cost of computational complexity, we empirically sho…

reinforcement-learning Reinforcement Learning (RL)

Feedback-Based Tree Search for Reinforcement Learning

2018-05-15 · ICML 2018 7 · Daniel R. Jiang, Emmanuel Ekwedike, Han Liu

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS…

AI Agent Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

2022-05-24 · Hassan Fawaz, Julien Lesca, Pham Tran Anh Quang, Jérémie Leguay 외

In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified …

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Coordinated Exploration in Concurrent Reinforcement Learning

2018-02-05 · ICML 2018 7 · Maria Dimakopoulou, Benjamin Van Roy

We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coo…

Diversity reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)