[DL輪読会]Composable Deep Reinforcement Learning for Robotic Manipulation

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April 06, 18

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2018/04/06
Deep Learning JP:
http://deeplearning.jp/seminar-2/

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1 DEEP LEARNING JP [DL Papers] “Composable Deep Reinforcement Learning for Robotic Manipulation” Zero-Shot Iori Yanokura Visual Imitation (ICLR 2018) http://deeplearning.jp/

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• https://github.com/haarnoja/softqlearning

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• Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, and Sergey Levine

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Maximum Entropy RL    Maximum Entropy  Entropy    http://bair.berkeley.edu/blog/2017/10/06/soft-q-learning/  (Ziebart 2010) 3

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4 Soft Q-Learning     Q  Soft Bellman Equation (Haarnoja, 2017)   

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COMPOSITIONALITY OF MAXIMUM
ENTROPY POLICIES
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COMPOSITIONALITY OF MAXIMUM
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Bounding the Sub-Optimality of Composed Policies Appendix A KL-divergence Qc*overestimate    7

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Bounding the Sub-Optimality of Composed Policies D , γC*   8

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9 Experiments • Simulation  • MuJoCo • 7-DoF Sawyer Robot • Actions • Torque command at each joint • Observations •   • Q-function, policy • 100 or 200 unit  

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B. Composing Policies for Pushing in Simulation • MuJoCo  • /    •     10

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C. SQL for Real-World Manipulation • Sawyer" • • • • Reaching Stack policy Avoid an obstacle 2compositionality of soft policies Reaching Lego Stacking 2   ('$& ( %$#" !. RL) 11

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C. SQL for Real-World Manipulation compositionality of soft policies Avoid a fixed obstacle policy  Lego Stacking policy || Composing Policies  https://www.youtube.com/watch?time_continue=10&v=wdexoLS2cWU 12

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