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Guided policy search is a method for reinforcement learning that trains a general policy for accomplishing a given task by guiding the learning of the policy with multiple guiding distributions.
Differential dynamic programming
Jacobson, D., Mayne, D.: · 1970
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Linear optimal control systems. Volume 1
Kwakernaak, H., Sivan, R.: · 1972
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Automatic discovery of subgoals in reinforcement learning using diverse density
McGovern, A., Barto, A.G.: · 2001
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Sloshing suppression control of automatic pouring robot by hybrid shape approach
Yano, K., Toda, T., Terashima, K.: · 2001
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Multiagent learning using a variable learning rate
Bowling, M., Veloso, M.: · 2002
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Effective reinforcement learning for mobile robots
Smart, W.D., Kaelbling, L.P.: · 2002
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Policy gradient reinforcement learning for fast quadrupedal locomotion
Kohl, N., Stone, P.: · 2004
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Finite mixture models
McLachlan, G., Peel, D.: · 2004
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Neural fitted Q iteration–First experiences with a data efficient neural reinforcement learning method
Riedmiller, M.: · 2005
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Vision based behavior verification system of humanoid robot for daily environment tasks
Okada, K., Kojima, M., Sagawa, Y., Ichino, T., Sato, K., Inaba, M.: · 2006
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Skill discovery in continuous reinforcement learning domains using skill chaining
Konidaris, G., Barreto, A.S.: · 2009
Cited alongside, same era.
Constructing skill trees for reinforcement learning agents from demonstration trajectories
Konidaris, G., Kuindersma, S., Grupen, R., Barreto, A.S.: · 2010
Cited alongside, same era.
Autonomous Robot Skill Acquisition
Konidaris, G.: · 2011
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Pilco: A model-based and data-efficient approach to policy search
Deisenroth, M., Rasmussen, C.E.: · 2011
Cited alongside, same era.
Designing robot learners that ask good questions
Cakmak, M., Thomaz, A.L.: · 2012
Cited alongside, same era.
Guided policy search
Levine, S., Koltun, V.: · 2013
Cited alongside, same era.
Reinforcement learning
Alpaydin, E.: · 2014
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Incorporating failure-to-success transitions in imitation learning for a dynamic pouring task
Langsfeld, J., Kaipa, K., Gentili, R., Reggia, J., Gupta, S.: · 2014
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Control-limited differential dynamic programming
Tassa, Y., Mansard, N., Todorov, E.: · 2014
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Combining the benefits of function approximation and trajectory optimization
Mordatch, I., Todorov, E.: · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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End-to-end training of deep visuomotor policies
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: · 2013
Cited alongside, same era.
Force-based robot learning of pouring skills using parametric hidden markov models
Rozo, L., Jimenez, P., Torras, C.: · 2013
Cited alongside, same era.
Semantically Grounded Learning from Unstructured Demonstrations
Niekum, S.: · 2013
Cited alongside, same era.
An integrated system for learning multi-step robotic tasks from unstructured demonstrations
Niekum, S.: · 2013
Cited alongside, same era.
Levine, S., Finn, C., Darrell, T., Abbeel, P.: · 2015
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Differential dynamic programming with temporally decomposed dynamics
Yamaguchi, A., Atkeson, C.G.: · 2015
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Gaussian processes for data-efficient learning in robotics and control
Deisenroth, M.P., Fox, D., Rasmussen, C.E.: · 2015
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Matlab statistics and machine learning toolbox
: · 2015
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