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Reinforcement learning has shown great promise in robotics thanks to its ability to develop efficient robotic control procedures through self-training.
International Conference on Robotics and Automation (ICRA) (2019)
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In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (2020)
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Nature 518
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31st International Conference on Machine Learning (ICML 2015) (2015)
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Nature 529
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4th International Conference on Learning Representation (2016)
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In: 33rd International Conference on Machine Learning (ICML), vol. 48, pp. 1928–1937 (2016)
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Advances in Neural Information Processing Systems 30 (NIPS) (2017)
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Proceedings - IEEE International Conference on Robotics and Automation pp. 2169–2176 (2017)
Devin, C., Gupta, A., Darrell, T., Abbeel, P., Levine, S.: Learning Modular Neural Network Policies for Multi-task and Multi-robot Transfer · 2017
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International Conference on Learning Representations (2017)
Gupta, A., Devin, C., Liu, Y., Abbeel, P., Levine, S.: Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning · 2017
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International Conference on Machine Learning (2018)
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor · 2018
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Computing Research Repository CoRR (2018)
Plappert, M., Andrychowicz, M., Ray, A., McGrew, B., Baker, B., Powell, G., Schneider, J., Tobin, J., Chociej, M., Welinder, P., Kumar, V., Zaremba, W.: Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research · 2018
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32nd Conference on Neural Information Processing Systems (NeurIPS) (2018)
Chen, T., Murali, A., Gupta, A.: Hardware Conditioned Policies for Multi-Robot Transfer Learning · 2018
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IEEE International Conference on Intelligent Robots and Systems pp. 4635–4640 (2018)
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Tavakoli, A., Pardo, F., Kormushev, P.: Action Branching Architectures for Deep Reinforcement Learning · 2017
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31st Conference on Neural Information Processing Systems (NIPS) pp. 5280–5289 (2017)
Wu, Y., Mansimov, E., Liao, S., Grosse, R., Ba, J.: Scalable Trust-Region Method for Deep Reinforcement Learning Using Kronecker-Factored Approximation · 2017
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Computing Research Repository (CoRR) pp. 1–12 (2017)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms · 2017
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops pp. 488–489 (2017)
Pathak, D., Agrawal, P., Efros, A.A., Darrell, T.: Curiosity-Driven Exploration by Self-Supervised Prediction · 2017
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35th International Conference on Machine Learning, ICML 2018 4
Fujimoto, S., Van Hoof, H., Meger, D.: Addressing Function Approximation Error in Actor-Critic Methods · 2018
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Computing Research Repository CoRR (2018)
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., Levine, S.: Soft Actor-Critic Algorithms and Applications · 2018
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Trossen Robotics (website accessed on 02/07/20)
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Proceedings - IEEE International Conference on Robotics and Automation pp. 6236–6243 (2018)
Pham, T.H., De Magistris, G., Tachibana, R.: OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World · 2018
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6th International Conference on Learning Representations (ICLR) pp. 1–14 (2018)
Pong, V., Gu, S., Dalal, M., Levine, S.: Temporal difference models: Model-free deep RL for model-based control · 2018
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Computing Research Repository CoRR (2018)
Pinto, L., Mandalika, A., Hou, B., Srinivasa, S.: Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions · 2018
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GitHub repository (2018)
Hill, A., Raffin, A., Ernestus, M., Gleave, A., Kanervisto, A., Traore, R., Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., Wu, Y.: Stable Baselines · 2018
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Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 2623–2631 (2019)
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework · 2019
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International Joint Conference on Neural Networks (IJCNN) pp. 1–8 (2020)
Luo, S., Kasaei, H., Schomaker, L.: Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning · 2020
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