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Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed.
Some topics in two-person games
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Markov decision processes
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Optimizing the cvar via sampling
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A. Rajeswaran, S. Ghotra, B. Ravindran, and S. Levine · 2016
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Cad2rl: Real single-image flight without a single real image
F. Sadeghi and S. Levine · 2016
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Reinforcement learning for pivoting task
R. Antonova, S. Cruciani, C. Smith, and D. Kragic · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Deep online learning via meta-learning: Continual adaptation for model-based rl
A. Nagabandi, C. Finn, and S. Levine · 2018
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A. Gleave, M. Dennis, C. Wild, N. Kant, S. Levine, and S. Russell · 2019
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A unified game-theoretic approach to multiagent reinforcement learning
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Action robust reinforcement learning and applications in continuous control
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
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Real world games look like spinning tops
W. M. Czarnecki, G. Gidel, B. Tracey, K. Tuyls, S. Omidshafiei, D. Balduzzi, and M. Jaderberg · 2020
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Robust reinforcement learning via adversarial training with langevin dynamics
P. Kamalaruban, Y.-T. Huang, Y.-P. Hsieh, P. Rolland, C. Shi, and V. Cevher · 2020
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