Acme: A Research Framework for Distributed Reinforcement Learning

Type: Preprint

Publication Date: 2020-01-01

Citations: 38

DOI: https://doi.org/10.48550/arxiv.2006.00979

Locations

  • arXiv (Cornell University) - View - PDF
  • DataCite API - View

Similar Works

Action Title Year Authors
+ Acme: A Research Framework for Distributed Reinforcement Learning 2020 Matt Hoffman
Bobak Shahriari
John Aslanides
Gabriel Barth-Maron
Feryal Behbahani
Tamara Norman
Abbas Abdolmaleki
Albin Cassirer
Fan Yang
Kate Baumli
+ Mava: a research framework for distributed multi-agent reinforcement learning. 2021 Arnu Pretorius
Kale-ab Tessera
Andries P. Smit
Claude Formanek
St John Grimbly
Kevin Eloff
Siphelele Danisa
Lawrence Francis
Jonathan P. Shock
Herman Kamper
+ SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning 2019 Keng Wah Loon
Laura Graesser
Milan Cvitkovic
+ SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning 2019 Keng Wah Loon
Laura Graesser
Milan Cvitkovic
+ Integrating Distributed Architectures in Highly Modular RL Libraries 2020 Albert Bou
Gianni De Fabritiis
+ A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots 2019 Nicolai A. Lynnerup
Laura Nolling
Rasmus Hasle
John Hallam
+ A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots 2019 Nicolai A. Lynnerup
Laura Nolling
Rasmus Hasle
John Hallam
+ rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch 2019 Adam Stooke
Pieter Abbeel
+ RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library. 2020 Zihan Ding
Tianyang Yu
Yanhua H. Huang
Hongming Zhang
Luo Mai
Hao Dong
+ Behaviour Suite for Reinforcement Learning 2020 Ian Osband
Yotam Doron
Matteo Hessel
John Aslanides
Eren Sezener
André Saraiva
Katrina McKinney
Tor Lattimore
Csaba Szepezvari
Satinder Singh
+ Behaviour Suite for Reinforcement Learning 2019 Ian Osband
Yotam Doron
Matteo Hessel
John Aslanides
Eren Sezener
André Saraiva
Katrina McKinney
Tor Lattimore
Csaba Szepesvári
Satinder Singh
+ PDF Chat ChainerRL: A Deep Reinforcement Learning Library 2019 Yasuhiro Fujita
Prabhat Nagarajan
Toshiki Kataoka
Takahiro Ishikawa
+ ChainerRL: A Deep Reinforcement Learning Library 2019 Yasuhiro Fujita
Prabhat Nagarajan
Toshiki Kataoka
Takahiro Ishikawa
+ TorchBeast: A PyTorch Platform for Distributed RL 2019 Heinrich KĂĽttler
Nantas Nardelli
Thibaut Lavril
Marco Selvatici
Viswanath Sivakumar
Tim Rocktäschel
Edward Grefenstette
+ rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch. 2019 Adam Stooke
Pieter Abbeel
+ JORLDY: a fully customizable open source framework for reinforcement learning 2022 Kyushik Min
Hyunho Lee
Kwansu Shin
Taehak Lee
Hojoon Lee
Jinwon Choi
Sungho Son
+ Behaviour Suite for Reinforcement Learning 2019 Ian Osband
Yotam Doron
Matteo Hessel
John Aslanides
Eren Sezener
André Saraiva
Katrina McKinney
Tor Lattimore
Csaba Szepesvári
Satinder Singh
+ Mava: a research library for distributed multi-agent reinforcement learning in JAX 2021 Ruan de Kock
Omayma Mahjoub
Sasha Abramowitz
Wiem Khlifi
Callum Rhys Tilbury
Claude Formanek
Andries Smit
Arnu Pretorius
+ Catalyst.RL: A Distributed Framework for Reproducible RL Research 2019 Sergey Kolesnikov
Oleksii Hrinchuk
+ SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores 2023 Zhiyu Mei
Wei Fu
Guangju Wang
Huanchen Zhang
Yi Wu

Works That Cite This (14)

Action Title Year Authors
+ PDF Chat Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning Toward Asynchronous Commercial Games 2022 Hui Bai
Ruimin Shen
Yue Lin
Botian Xu
Ran Cheng
+ PDF Chat Transductive Reward Inference on Graph 2024 Bohao Qu
Xiaofeng Cao
Qing Guo
Yi Chang
Ivor W. Tsang
Chengqi Zhang
+ DRLinFluids -- An open-source python platform of coupling Deep Reinforcement Learning and OpenFOAM 2022 Qiulei Wang
Lei Yan
Gang Hu
Chao Li
Yiqing Xiao
Hao Xiong
Jean Rabault
Bernd R. Noack
+ PDF Chat NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields 2023 Arunkumar Byravan
Jan Humplik
Leonard Hasenclever
Arthur Brussee
Francesco Nori
Tuomas Haarnoja
Ben Moran
Steven Bohez
Fereshteh Sadeghi
Bojan Vujatovic
+ PDF Chat Multi-Agent Deep Reinforcement Learning for Distributed Load Restoration 2023 Linh Vu
Tuyen Vu
Thanh Long Vu
Anurag K. Srivastava
+ Reinforcement Learning for Test Case Prioritization 2021 Mojtaba Bagherzadeh
Nafıseh Kahani
Lionel Briand
+ PDF Chat AccMER: Accelerating Multi-Agent Experience Replay with Cache Locality-Aware Prioritization 2023 Kailash Gogineni
Yongsheng Mei
LĂĽ Tian
Wei Peng
Guru Venkataramani
+ Robotic Table Tennis: A Case Study into a High Speed Learning System 2023 David B. D’Ambrosio
Navdeep Jaitly
Vikas Sindhwani
Ken Oslund
Peng Xu
Nevena Lazic
Anish Shankar
Tianli Ding
Jonathan Abelian
Erwin Coumans
+ Robotic Table Tennis: A Case Study into a High Speed Learning System 2023 David B. D’Ambrosio
Jonathan Abelian
Saminda Abeyruwan
Michael J. Ahn
Alex Bewley
Justin D. Boyd
Krzysztof Choromański
Omar Andrés Carmona Cortes
Erwin Coumans
Tianli Ding
+ PDF Chat SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems 2024 Kailash Gogineni
Sai Santosh Dayapule
Juan GĂłmez-Luna
Karthikeya Gogineni
Wei Peng
LĂĽ Tian
Mohammad Sadrosadati
Onur Cezmi Mutlu
Guru Venkataramani

Works Cited by This (0)

Action Title Year Authors