TorchRL: A data-driven decision-making library for PyTorch
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Shagun Sodhani | G. D. Fabritiis | V. Moens | Matteo Bettini | Xiaomeng Yang | S. Dittert | Vishal Kumar | Albert Bou | Vikash Kumar
[1] S. Levine,et al. Offline Reinforcement Learning for Visual Navigation , 2022, CoRL.
[2] Vikash Kumar,et al. MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations , 2022, ICLR.
[3] Scott A. Munns,et al. Controlling Commercial Cooling Systems Using Reinforcement Learning , 2022, ArXiv.
[4] Aja Huang,et al. Discovering faster matrix multiplication algorithms with reinforcement learning , 2022, Nature.
[5] Yecheng Jason Ma,et al. VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training , 2022, ICLR.
[6] Amanda Prorok,et al. VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning , 2022, DARS.
[7] P. Abbeel,et al. DayDreamer: World Models for Physical Robot Learning , 2022, CoRL.
[8] Vikash Kumar,et al. R3M: A Universal Visual Representation for Robot Manipulation , 2022, CoRL.
[9] Xiaolong Wang,et al. Temporal Difference Learning for Model Predictive Control , 2022, ICML.
[10] Amy Zhang,et al. Online Decision Transformer , 2022, ICML.
[11] Jeff Wu,et al. WebGPT: Browser-assisted question-answering with human feedback , 2021, ArXiv.
[12] Ben M. Hambly,et al. Recent advances in reinforcement learning in finance , 2021, SSRN Electronic Journal.
[13] Sergey Levine,et al. Offline Reinforcement Learning with Implicit Q-Learning , 2021, ICLR.
[14] Philipp Reist,et al. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning , 2021, CoRL.
[15] Jun Zhu,et al. Tianshou: a Highly Modularized Deep Reinforcement Learning Library , 2021, J. Mach. Learn. Res..
[16] Angel X. Chang,et al. Habitat 2.0: Training Home Assistants to Rearrange their Habitat , 2021, NeurIPS.
[17] Olivier Bachem,et al. Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation , 2021, NeurIPS Datasets and Benchmarks.
[18] Pieter Abbeel,et al. Decision Transformer: Reinforcement Learning via Sequence Modeling , 2021, NeurIPS.
[19] Quoc V. Le,et al. A graph placement methodology for fast chip design , 2021, Nature.
[20] Ramin M. Hasani,et al. Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[21] Yu Wang,et al. The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games , 2021, NeurIPS.
[22] Che Wang,et al. Randomized Ensembled Double Q-Learning: Learning Fast Without a Model , 2021, ICLR.
[23] Shimon Whiteson,et al. Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? , 2020, ArXiv.
[24] Yuval Tassa,et al. dm_control: Software and Tasks for Continuous Control , 2020, Softw. Impacts.
[25] Sergio Gomez Colmenarejo,et al. Acme: A Research Framework for Distributed Reinforcement Learning , 2020, ArXiv.
[26] Justin Fu,et al. D4RL: Datasets for Deep Data-Driven Reinforcement Learning , 2020, ArXiv.
[27] Amir Mosavi,et al. Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics , 2020, Mathematics.
[28] Shimon Whiteson,et al. Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning , 2020, J. Mach. Learn. Res..
[29] Szil'ard Aradi,et al. Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles , 2020, IEEE Transactions on Intelligent Transportation Systems.
[30] Andrea Bonarini,et al. MushroomRL: Simplifying Reinforcement Learning Research , 2020, J. Mach. Learn. Res..
[31] Jakub W. Pachocki,et al. Dota 2 with Large Scale Deep Reinforcement Learning , 2019, ArXiv.
[32] Yasuhiro Fujita,et al. ChainerRL: A Deep Reinforcement Learning Library , 2019, J. Mach. Learn. Res..
[33] Jimmy Ba,et al. Dream to Control: Learning Behaviors by Latent Imagination , 2019, ICLR.
[34] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[35] Manuela Veloso,et al. Reinforcement Learning for Market Making in a Multi-agent Dealer Market , 2019, ArXiv.
[36] Ari S. Morcos,et al. DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames , 2019, ICLR.
[37] Marcin Andrychowicz,et al. Solving Rubik's Cube with a Robot Hand , 2019, ArXiv.
[38] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[39] Pieter Abbeel,et al. rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch , 2019, ArXiv.
[40] Sriram Srinivasan,et al. OpenSpiel: A Framework for Reinforcement Learning in Games , 2019, ArXiv.
[41] O. Bousquet,et al. Google Research Football: A Novel Reinforcement Learning Environment , 2019, AAAI.
[42] Jiaxing Song,et al. Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems , 2019, KDD.
[43] Shimon Whiteson,et al. The StarCraft Multi-Agent Challenge , 2019, AAMAS.
[44] Henry Zhu,et al. Soft Actor-Critic Algorithms and Applications , 2018, ArXiv.
[45] Demis Hassabis,et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.
[46] Xiaohui Ye,et al. Horizon: Facebook's Open Source Applied Reinforcement Learning Platform , 2018, ArXiv.
[47] Rémi Munos,et al. Recurrent Experience Replay in Distributed Reinforcement Learning , 2018, ICLR.
[48] Marc G. Bellemare,et al. Dopamine: A Research Framework for Deep Reinforcement Learning , 2018, ArXiv.
[49] Sergey Levine,et al. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation , 2018, CoRL.
[50] Weinan Zhang,et al. Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising , 2018, CIKM.
[51] Herke van Hoof,et al. Addressing Function Approximation Error in Actor-Critic Methods , 2018, ICML.
[52] Dan Horgan,et al. Distributed Prioritized Experience Replay , 2018, ICLR.
[53] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[54] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[55] Michael I. Jordan,et al. RLlib: Abstractions for Distributed Reinforcement Learning , 2017, ICML.
[56] Michael I. Jordan,et al. Ray: A Distributed Framework for Emerging AI Applications , 2017, OSDI.
[57] Olexandr Isayev,et al. Deep reinforcement learning for de novo drug design , 2017, Science Advances.
[58] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[59] Marcin Andrychowicz,et al. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[60] Tom Schaul,et al. Rainbow: Combining Improvements in Deep Reinforcement Learning , 2017, AAAI.
[61] Wei Wu,et al. Practical Block-Wise Neural Network Architecture Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[62] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[63] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[64] Marc G. Bellemare,et al. A Distributional Perspective on Reinforcement Learning , 2017, ICML.
[65] Yuandong Tian,et al. ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games , 2017, NIPS.
[66] Joel Z. Leibo,et al. Value-Decomposition Networks For Cooperative Multi-Agent Learning , 2017, ArXiv.
[67] Shane Legg,et al. Deep Reinforcement Learning from Human Preferences , 2017, NIPS.
[68] Yi Wu,et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.
[69] Etienne Perot,et al. Deep Reinforcement Learning framework for Autonomous Driving , 2017, Autonomous Vehicles and Machines.
[70] P. Abbeel,et al. Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[71] Jun Wang,et al. Real-Time Bidding by Reinforcement Learning in Display Advertising , 2017, WSDM.
[72] Ramesh Raskar,et al. Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.
[73] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[74] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[75] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[76] Luís Torgo,et al. OpenML: networked science in machine learning , 2014, SKDD.
[77] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[78] Neil Immerman,et al. The Complexity of Decentralized Control of Markov Decision Processes , 2000, UAI.
[79] Michael L. Littman,et al. Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.
[80] Rousslan Fernand Julien Dossa,et al. CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms , 2022, J. Mach. Learn. Res..
[81] A. Gleave,et al. Stable-Baselines3: Reliable Reinforcement Learning Implementations , 2021, J. Mach. Learn. Res..
[82] M. Tan,et al. Multi Agent Reinforcement Learning Independent vs Cooperative Agents , 2003 .