Asynchronous Coagent Networks

2019-02-15 · ICML 2020 1 · James E. Kostas, Chris Nota, Philip S. Thomas

Coagent policy gradient algorithms (CPGAs) are reinforcement learning algorithms for training a class of stochastic neural networks called coagent networks. In this work, we prove that CPGAs converge to locally optimal policies. Additionally, we extend prior theory to encompass asynchronous and recurrent coagent networks. These extensions facilitate the straightforward design and analysis of hierarchical reinforcement learning algorithms like the option-critic, and eliminate the need for complex derivations of customized learning rules for these algorithms.

📄 PDF Abstract BibTeX arXiv:1902.05650

Code (0)

등록된 구현이 없습니다.

Tasks

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Coagent Networks Revisited

2020-01-28 · Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer, Ahmadreza Moradipari 외

Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment. Prominent examples of coagent networks in action include approac…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

Coagent Networks: Generalized and Scaled

2023-05-16 · James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous 외

Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework o…

MuJoCo Reinforcement Learning (RL)

Edge-Compatible Reinforcement Learning for Recommendations

2021-12-10 · James E. Kostas, Philip S. Thomas, Georgios Theocharous

Most reinforcement learning (RL) recommendation systems designed for edge computing must either synchronize during recommendation selection or depend on an unprincipled patchwork collection of algorithms. In this work, w…

Edge-computing Recommendation Systems reinforcement-learning Reinforcement Learning+1

COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space

2026-05-20 · Oleksandr Yakovenko, Mahdi Mostajabdaveh, Cheikh Ahmed, Abdullah Ali Sivas 외 arxiv

Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditional heuristics rely on handcrafted rules f…

EcoAgent: An Efficient Edge-Cloud Collaborative Multi-Agent Framework for Mobile Automation

2025-05-08 · Biao Yi, Xavier Hu, Yurun Chen, Shengyu Zhang 외

Cloud-based mobile agents powered by (multimodal) large language models ((M)LLMs) offer strong reasoning abilities but suffer from high latency and cost. While fine-tuned (M)SLMs enable edge deployment, they often lose g…