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Reinforcement learning solely from an agent's self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed.
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N. Heess, G. Wayne, D. Silver, T. Lillicrap, T. Erez, and Y. Tassa, “Learning continuous control policies by stochastic value gradients,” Advances in Neural Information Processing Systems , 2015
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Y. Teh, V. Bapst, W. M. Czarnecki, J. Quan, J. Kirkpatrick, R. Hadsell, N. Heess, and R. Pascanu, “Distral: Robust multitask reinforcement learning,” Advances in neural information processing systems , vol. 30, 2017
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M. Riedmiller, R. Hafner, T. Lampe, M. Neunert, J. Degrave, T. Wiele, V. Mnih, N. Heess, and J. T. Springenberg, “Learning by playing solving sparse reward tasks from scratch,” in International conference on machine learning . PMLR, 2018, pp. 4344–4353
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T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International Conference on Machine Learning , 2018
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D. Kalashnikov, J. Varley, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, and K. Hausman, “Scaling up multi-task robotic reinforcement learning,” in 5th Annual Conference on Robot Learning , 2021
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S. Fujimoto, D. Meger, and D. Precup, “Off-policy deep reinforcement learning without exploration,” in International conference on machine learning , 2019
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2023
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2023
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