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In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms.
MuJoCo: A Physics Engine for Model-Based Control
E. Todorov, T. Erez, and Y. Tassa · 2012
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The Arcade Learning Environment: An Evaluation Platform for General Agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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Playing Atari with Deep Reinforcement Learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller · 2013
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Trust Region Policy Optimization
J. Schulman, S. Levine, P. Abbeel, M. I. Jordan, and P. Moritz · 2015
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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PyBullet, a Python Module for Physics Simulation for Games, Robotics and Machine Learning
E. Coumans and Y. Bai · 2016
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Asynchronous Methods for Deep Reinforcement Learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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High-Dimensional Continuous Control Using Generalized Advantage Estimation
J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel · 2016
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OpenAI Baselines
P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, Y. Wu, and P. Zhokhov · 2017
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Tensorforce: a TensorFlow library for applied reinforcement learning
A. Kuhnle, M. Schaarschmidt, and K. Fricke · 2017
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Proximal Policy Optimization Algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Spinning Up in Deep Reinforcement Learning
J. Achiam · 2018
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Dopamine: A Research Framework for Deep Reinforcement Learning
P. S. Castro, S. Moitra, C. Gelada, S. Kumar, and M. G. Bellemare · 2018
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Implicit Quantile Networks for Distributional Reinforcement Learning
W. Dabney, G. Ostrovski, D. Silver, and R. Munos · 2018
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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, S. Legg, and K. Kavukcuoglu · 2018
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Addressing Function Approximation Error in Actor-Critic Methods
S. Fujimoto, H. van Hoof, and D. Meger · 2018
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TF-Agents: A library for Reinforcement Learning in TensorFlow
S. Guadarrama, A. Korattikara, O. Ramirez, P. Castro, E. Holly, S. Fishman, K. Wang, E. Gonina, N. Wu, E. Kokiopoulou, L. Sbaiz, J. Smith, G. Bartók, J. Berent, C. Harris, V. Vanhoucke, and E. Brevdo · 2018
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Deep Reinforcement Learning That Matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2018
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Rainbow: Combining Improvements in Deep Reinforcement Learning
M. Hessel, J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. G. Azar, and D. Silver · 2018
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An Environment for Autonomous Driving Decision-Making
E. Leurent · 2018
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RLlib: Abstractions for Distributed Reinforcement Learning
E. Liang, R. Liaw, R. Nishihara, P. Moritz, R. Fox, K. Goldberg, J. Gonzalez, M. I. Jordan, and I. Stoica · 2018
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Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
M. C. Machado, M. G. Bellemare, E. Talvitie, J. Veness, M. J. Hausknecht, and M. Bowling · 2018
Cited alongside, same era.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
M. Plappert, M. Andrychowicz, A. Ray, B. McGrew, B. Baker, G. Powell, J. Schneider, J. Tobin, M. Chociej, P. Welinder, V. Kumar, and W. Zaremba · 2018
Cited alongside, same era.
Exploration by random network distillation
Y. Burda, H. Edwards, A. J. Storkey, and O. Klimov · 2019
Cited alongside, same era.
Garage: A toolkit for reproducible reinforcement learning research
T. garage contributors · 2019
Cited alongside, same era.
When to Trust Your Model: Model-Based Policy Optimization
M. Janner, J. Fu, M. Zhang, and S. Levine · 2019
Cited alongside, same era.
ChainerRL: A Deep Reinforcement Learning Library
Y. Fujita, P. Nagarajan, T. Kataoka, and T. Ishikawa · 2021
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panda-gym: Open-Source Goal-Conditioned Environments for Robotic Learning
Q. Gallouédec, N. Cazin, E. Dellandréa, and L. Chen · 2021
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JAXRL: Implementations of Reinforcement Learning algorithms in JAX
I. Kostrikov · 2021
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rl-games: A High-performance Framework for Reinforcement Learning
D. Makoviichuk and V. Makoviychuk · 2021
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Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
J. Pineau, P. Vincent-Lamarre, K. Sinha, V. Larivière, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and H. Larochelle · 2021
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Stable-Baselines3: Reliable Reinforcement Learning Implementations
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H. Küttler, N. Nardelli, T. Lavril, M. Selvatici, V. Sivakumar, T. Rocktäschel, and E. Grefenstette · 2019
Cited alongside, same era.
A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots
N. A. Lynnerup, L. Nolling, R. Hasle, and J. Hallam · 2019
Cited alongside, same era.
Is Deep Reinforcement Learning Really Superhuman on Atari?
M. Toromanoff, É. Wirbel, and F. Moutarde · 2019
Cited alongside, same era.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
M. Andrychowicz, A. Raichuk, P. Stanczyk, M. Orsini, S. Girgin, R. Marinier, L. Hussenot, M. Geist, O. Pietquin, M. Michalski, S. Gelly, and O. Bachem · 2020
Cited alongside, same era.
Agent57: Outperforming the Atari Human Benchmark
A. P. Badia, B. Piot, S. Kapturowski, P. Sprechmann, A. Vitvitskyi, Z. D. Guo, and C. Blundell · 2020
Cited alongside, same era.
Never Give Up: Learning Directed Exploration Strategies
A. P. Badia, P. Sprechmann, A. Vitvitskyi, Z. D. Guo, B. Piot, S. Kapturowski, O. Tieleman, M. Arjovsky, A. Pritzel, A. Bolt, and C. Blundell · 2020
Cited alongside, same era.
Experiment Tracking with Weights and Biases, 2020
L. Biewald · 2020
Cited alongside, same era.
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann · 2021
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Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
R. Agarwal, M. Schwarzer, P. S. Castro, A. C. Courville, and M. G. Bellemare · 2022
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MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
L. N. Alegre, F. Felten, E.-G. Talbi, G. Danoy, A. Nowé, A. L. C. Bazzan, and B. C. da Silva · 2022
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The 37 Implementation Details of Proximal Policy Optimization
S. Huang, R. F. J. Dossa, A. Raffin, A. Kanervisto, and W. Wang · 2022
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Multi-Game Decision Transformers
K. Lee, O. Nachum, M. Yang, L. Lee, D. Freeman, S. Guadarrama, I. Fischer, W. Xu, E. Jang, H. Michalewski, and I. Mordatch · 2022
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moolib: A Platform for Distributed RL
V. Mella, E. Hambro, D. Rothermel, and H. Küttler · 2022
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A Generalist Agent
S. E. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar, and N. de Freitas · 2022
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EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
J. Weng, M. Lin, S. Huang, B. Liu, D. Makoviichuk, V. Makoviychuk, Z. Liu, Y. Song, T. Luo, Y. Jiang, Z. Xu, and S. Yan · 2022
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abcdRL: Modular Single-file Reinforcement Learning Algorithms Library
Y. Zhao · 2022
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TorchRL: A Data-Driven Decision-Making Library for Pytorch
A. Bou, M. Bettini, S. Dittert, V. Kumar, S. Sodhani, X. Yang, G. D. Fabritiis, and V. Moens · 2023
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M. Chevalier-Boisvert, B. Dai, M. Towers, R. de Lazcano, L. Willems, S. Lahlou, S. Pal, P. S. Castro, and J. Terry · 2023
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A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
F. Felten, L. N. Alegre, A. Nowe, A. L. C. Bazzan, E. G. Talbi, G. Danoy, and B. C. da Silva · 2023
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Mastering Diverse Domains through World Models
D. Hafner, J. Pasukonis, J. Ba, and T. Lillicrap · 2023
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Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform, 2023
S. Huang, J. Weng, R. Charakorn, M. Lin, Z. Xu, and S. Ontañón · 2023
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Empirical Design in Reinforcement Learning
A. Patterson, S. Neumann, M. White, and A. White · 2023
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Gymnasium, Mar. 2023
M. Towers, J. K. Terry, A. Kwiatkowski, J. U. Balis, G. d. Cola, T. Deleu, M. Goulão, A. Kallinteris, A. KG, M. Krimmel, R. Perez-Vicente, A. Pierré, S. Schulhoff, J. J. Tai, A. T. J. Shen, and O. G. Younis · 2023
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