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Reinforcement learning (RL) agents have long sought to approach the efficiency of human learning.
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Multi-goal reinforcement learning: Challenging robotics environments and request for research
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Jaekyeom Kim, Seohong Park, and Gunhee Kim · 2021
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Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation
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Provably efficient safe exploration via primal-dual policy optimization
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Reinforcement learning under moral uncertainty
Adrien Ecoffet and Joel Lehman · 2021
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Reincarnating reinforcement learning: Reusing prior computation to accelerate progress
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2022
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, et al · 2022
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Outracing champion gran turismo drivers with deep reinforcement learning
Peter R Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, et al · 2022
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Robot reinforcement learning on the constraint manifold
Puze Liu, Davide Tateo, Haitham Bou Ammar, and Jan Peters · 2022
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