Fetching the paper…
Reading the bibliography…
Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies.
Torcs, the open racing car simulator
Bernhard Wymann, Eric Espié, Christophe Guionneau, Christos Dimitrakakis, Rémi Coulom, and Andrew Sumner · 2000
Earlier work this paper cites.
Autonomous driving in urban environments: approaches, lessons and challenges
Mark Campbell, Magnus Egerstedt, Jonathan P How, and Richard M Murray · 2010
Earlier work this paper cites.
Safe exploration of state and action spaces in reinforcement learning
Javier Garcia and Fernando Fernández · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Safe exploration techniques for reinforcement learning–an overview
Martin Pecka and Tomas Svoboda · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
ViZDoom: A Doom-based AI research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
Earlier work this paper cites.
Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2017
Earlier work this paper cites.
Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
Earlier work this paper cites.
Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, and Shane Legg · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Starcraft ii: A new challenge for reinforcement learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, et al · 2017
Earlier work this paper cites.
Interactive narrative personalization with deep reinforcement learning
Pengcheng Wang, Jonathan P Rowe, Wookhee Min, Bradford W Mott, and James C Lester · 2017
Earlier work this paper cites.
Deep reinforcement learning for building hvac control
Tianshu Wei, Yanzhi Wang, and Qi Zhu · 2017
Earlier work this paper cites.
Safe reinforcement learning via shielding
Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, and Ufuk Topcu · 2018
Earlier work this paper cites.
Driving policy transfer via modularity and abstraction
Matthias Müller, Alexey Dosovitskiy, Bernard Ghanem, and Vladlen Koltun · 2018
Earlier work this paper cites.
Deep reinforcement learning for autonomous driving
Sen Wang, Daoyuan Jia, and Xinshuo Weng · 2018
Earlier work this paper cites.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Cited alongside, same era.
Curriculum-guided hindsight experience replay
Meng Fang, Tianyi Zhou, Yali Du, Lei Han, and Zhengyou Zhang · 2019
Cited alongside, same era.
A deep reinforcement learning network for traffic light cycle control
Xiaoyuan Liang, Xunsheng Du, Guiling Wang, and Zhu Han · 2019
Cited alongside, same era.
Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
Cited alongside, same era.
An empirical investigation of the challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Nir Levine, Daniel J Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, and Todd Hester · 2020
Levdoom: A benchmark for generalization on level difficulty in reinforcement learning
Tristan Tomilin, Tianhong Dai, Meng Fang, and Mykola Pechenizkiy · 2022
Later among the works it cites.
Safe-control-gym: A unified benchmark suite for safe learning-based control and reinforcement learning in robotics
Zhaocong Yuan, Adam W Hall, Siqi Zhou, Lukas Brunke, Melissa Greeff, Jacopo Panerati, and Angela P Schoellig · 2022
Later among the works it cites.
Penalized proximal policy optimization for safe reinforcement learning
Linrui Zhang, Li Shen, Long Yang, Shixiang Chen, Bo Yuan, Xueqian Wang, and Dacheng Tao · 2022
Later among the works it cites.
Mindagent: Emergent gaming interaction
Ran Gong, Qiuyuan Huang, Xiaojian Ma, Hoi Vo, Zane Durante, Yusuke Noda, Zilong Zheng, Song-Chun Zhu, Demetri Terzopoulos, Li Fei-Fei, et al · 2023
Later among the works it cites.
Madi: Learning to mask distractions for generalization in visual deep reinforcement learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning
Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, and Vladlen Koltun · 2020
Cited alongside, same era.
Comparing reinforcement learning methods for real-time optimization of a chemical process
Titus Quah, Derek Machalek, and Kody M Powell · 2020
Cited alongside, same era.
Responsive safety in reinforcement learning by pid lagrangian methods
Adam Stooke, Joshua Achiam, and Pieter Abbeel · 2020
Cited alongside, same era.
Safe reinforcement learning via curriculum induction
Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, and Alekh Agarwal · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
Cited alongside, same era.
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2021
Cited alongside, same era.
Sassi: safety analysis using simulation-based situation coverage for cobot systems
Benjamin Lesage and Rob Alexander · 2021
Cited alongside, same era.
Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E Taylor, A Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, and Decebal Constantin Mocanu · 2023
Later among the works it cites.
Safe multi-agent reinforcement learning for multi-robot control
Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang · 2023
Later among the works it cites.
Autonomous driving with deep reinforcement learning in carla simulation
Jumman Hossain · 2023
Later among the works it cites.
Raidenv: Exploring new challenges in automated content balancing for boss raid games
Hyeon-Chang Jeon, In-Chang Baek, Cheong-mok Bae, Taehwa Park, Wonsang You, Taegwan Ha, Hoyoun Jung, Jinha Noh, Seungwon Oh, and Kyung-Joong Kim · 2023
Later among the works it cites.
Reinforcement learning applications to machine scheduling problems: a comprehensive literature review
Behice Meltem Kayhan and Gokalp Yildiz · 2023
Later among the works it cites.
Neuralfield-ldm: Scene generation with hierarchical latent diffusion models
Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, and Sanja Fidler · 2023
Later among the works it cites.
Safe navigation: Training autonomous vehicles using deep reinforcement learning in carla
Ghadi Nehme and Tejas Y Deo · 2023
Later among the works it cites.
Stabilizing visual reinforcement learning via asymmetric interactive cooperation
Yunpeng Zhai, Peixi Peng, Yifan Zhao, Yangru Huang, and Yonghong Tian · 2023
Later among the works it cites.
Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo Perez-Vicente, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, and Jordan Terry · 2024
Later among the works it cites.
Reinforcement learning with ensemble model predictive safety certification
Sven Gronauer, Tom Haider, Felippe Schmoeller da Roza, and Klaus Diepold · 2024
Later among the works it cites.
Games for artificial intelligence research: A review and perspectives
Chengpeng Hu, Yunlong Zhao, Ziqi Wang, Haocheng Du, and Jialin Liu · 2024
Later among the works it cites.
Unveiling the significance of toddler-inspired reward transition in goal-oriented reinforcement learning
Junseok Park, Yoonsung Kim, Hee bin Yoo, Min Whoo Lee, Kibeom Kim, Won-Seok Choi, Minsu Lee, and Byoung-Tak Zhang · 2024
Later among the works it cites.
Coom: A game benchmark for continual reinforcement learning
Tristan Tomilin, Meng Fang, Yudi Zhang, and Mykola Pechenizkiy · 2024
Later among the works it cites.
Diffusion models are real-time game engines
Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter · 2024
Later among the works it cites.
Safe multi-agent reinforcement learning with natural language constraints
Ziyan Wang, Meng Fang, Tristan Tomilin, Fei Fang, and Yali Du · 2024
Later among the works it cites.