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RLlib: Abstractions for Distributed Reinforcement Learning

2017-12-26 · ICML 2018 7 · Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate the benefits of this principle through RLlib: a library that provides scalable software primitives for RL. These primitives enable a broad range of algorithms to be implemented with high performance, scalability, and substantial code reuse. RLlib is available at https://rllib.io/.

📄 PDF Abstract BibTeX arXiv:1712.09381

Code (3)

ray-project/ray 공식 구현 tf

AmeerHajAli/ray2 tf

susumuota/distributed_experience_replay tf

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)rllib

RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

2020-11-25 · NeurIPS 2021 12 · Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika 외

Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years. In this paper, we re-ex…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)rllib

Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach

2024-02-24 · Alaa Selim, Yanzhu Ye, Junbo Zhao, Bo Yang

In the rapidly evolving domain of electrical power systems, the Volt-VAR optimization (VVO) is increasingly critical, especially with the burgeoning integration of renewable energy sources. Traditional approaches to lear…

Deep Reinforcement Learning Distributed Computing reinforcement-learning Reinforcement Learning+1

MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

2022-10-11 · Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang 외

A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and alg…

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

Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning

2024-08-12 · Navid Keshtiarast, Oliver Renaldi, Marina Petrova

In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for Medium Access Control (MAC) protocol design. Unlike centralized approaches, which rely on a single entity for decision-maki…

Decision Making Deep Reinforcement Learning rllib

POPGym: Benchmarking Partially Observable Reinforcement Learning

2023-03-03 · Steven Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki 외

Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contemporary RL benchmarks and libraries. We i…

Benchmarking GPU Partially Observable Reinforcement Learning reinforcement-learning+4