deep rbf value functions for continuous control
play

Deep RBF Value Functions for Continuous Control Kavosh Asadi - PowerPoint PPT Presentation

Deep RBF Value Functions for Continuous Control Kavosh Asadi Ronald Parr George Konidaris Michael Littman 1 <latexit


  1. Deep RBF Value Functions for Continuous Control Kavosh Asadi Ronald Parr George Konidaris Michael Littman � 1

  2. <latexit sha1_base64="53B5SMtKkE+DzAK9yYgkFpOi08Q=">AB6nicbVDLSsNAFL2pr1pfVZduBovgQkpi49d0Y3LivYBbSiT6aQdOpmEmYlQj/BjQtF3PpF7vwbJ2kQtR64cDjnXu69x4s4U9q2P63C0vLK6lpxvbSxubW9U97da6swloS2SMhD2fWwopwJ2tJMc9qNJMWBx2nHm1ynfueBSsVCca+nEXUDPBLMZwRrI92pEzkoV+yqnQEtEicnFcjRHJQ/+sOQxAEVmnCsVM+xI+0mWGpGOJ2V+rGiESYTPKI9QwUOqHKT7NQZOjLKEPmhNCU0ytSfEwkOlJoGnukMsB6rv14q/uf1Yu1fuAkTUaypIPNFfsyRDlH6NxoySYnmU0MwkczcisgYS0y0SaeUhXCZ4uz75UXSPq06tWrtl5pXOVxFOEADuEYHDiHBtxAE1pAYASP8AwvFrerFfrbd5asPKZfgF6/0LMd+N4A=</latexit> <latexit sha1_base64="F2D/FvNhHLsAPKNyXxvNekAF7Nw=">AB6HicbVDJSgNBEK2JW4xb1KOXxiB4CjMqLregF48JmAWSIfR0apI2PQvdPUI+QIvHhTx6id582/smQyixgcFj/eqKrnxYIrbdufVmFpeWV1rbhe2tjc2t4p7+61VJRIhk0WiUh2PKpQ8BCbmuBnVgiDTyBbW98k/rtB5SKR+GdnsToBnQYcp8zqo3UoP1yxa7aGcgicXJSgRz1fvmjN4hYEmComaBKdR071u6USs2ZwFmplyiMKRvTIXYNDWmAyp1mh87IkVEGxI+kqVCTP05MaWBUpPAM50B1SP10vF/7xuov1Ld8rDONEYsvkiPxFERyT9mgy4RKbFxBDKJDe3EjaikjJtsilIVylOP9+eZG0TqrOafW0cVapXedxFOEADuEYHLiAGtxCHZrAOERnuHFurerFfrbd5asPKZfgF6/0L3CWNHA=</latexit> The RL Problem a agent s, r � 2

  3. “The state-space complexity for Go has been estimated at 10 174 , which is more than the total number of atoms in the universe.” � 3

  4. � 4

  5. <latexit sha1_base64="Eap0UWn/Y0YCL6OqYgDXYjRvcbI=">ACFnicbVDJSgNBEO2JW4xb1KOXwSBEiGFixA0CQRE9JmAWSMahp9OTNOlZ6K4Rwpiv8OKvePGgiFfx5t/YkwR4NuHu9VUVXPDjiTYBifWmJmdm5+IbmYWlpeWV1Lr2/UpR8KQmvE575o2lhSzjxaAwacNgNBsWtz2rD757HfuKVCMt+7hkFATRd3PeYwgkFJVnqvehO1AzbMyhzePS21XQw924kuhq1LC+6kBSVlqB/nVJVpTNG3hBnyaFCcmgCSpW+qPd8UnoUg8Ix1K2CkYAZoQFMLpMNUOJQ0w6eMubSnqYZdKMxqdNdR3lNLRHV+o54E+Un92RNiVcuDaqjJeW/71YvE/rxWCc2xGzAtCoB4ZD3JCroOvxnpHSYoAT5QBPB1K46WGBCagkU6MQTmIcfp8Ter7+UIxX6weZMpnkziSaAtoywqoCNURleogmqIoHv0iJ7Ri/agPWmv2tu4NKFNejbRL2jvX0dlnvY=</latexit> <latexit sha1_base64="SmPoZkPmiI4sBiQ+C+T6BVdgfPg=">ACGXicbVDLSgMxFM3UV62vUZdugkVwUcrUio9d1Y3Lqn1Bp5Q7adqGJpkhyQil9Dfc+CtuXCjiUlf+jTPTUtR6IHA451xy7/ECzrRxnC8rtbC4tLySXs2srW9sbtnbOzXth4rQKvG5rxoeaMqZpFXDKeNQFEQHqd1b3AV+/V7qjTzZcUMA9oS0JOsywiYSGrbjstB9jFrgDTJ8DxXW5GL3L4NlfJuT0QAlyVBNt21sk7CfA8KUxJFk1RbtsfbscnoaDSEA5aNwtOYFojUIYRTscZN9Q0ADKAHm1GVIKgujVKLhvjg0jp4K6voicNTtSfEyMQWg+FyXjpfVfLxb/85qh6Z61RkwGoaGSTD7qhwbH8c14Q5TlBg+jAgQxaJdMemDAmKiMjNJCecxTmYnz5PaUb5QzBdvjrOly2kdabSH9tEhKqBTVELXqIyqiKAH9IRe0Kv1aD1b9b7JqypjO76Besz2+rcJ+b</latexit> <latexit sha1_base64="rBfmq1u3Ee+W/A/4LPk3OZYW4zw=">AC3icbZDLSsNAFIYnXmu9RV26CS1CFSmJFW8gFN24bMFeoIlhMp20QycXZiZiCdm78VXcuFDErS/gzrdxkgZR6w/D/HznHGbO74SUcKHrn8rM7Nz8wmJhqbi8srq2rm5stnkQMYRbKAB6zqQY0p83BJEUNwNGYaeQ3HGV2m9c4tZpwE/rUYh9jy4MAnLkFQSGSrpeZNvJdU+D7cPTs3PXhnx2ZIEknTK+O2WtareiZt2hi5KYNcDVv9MPsBijzsC0Qh5z1D4UVQyYIojgpmhHIUQjOMA9aX3oYW7F2S6JtiNJX3MDJo8vtIz+nIihx/nYc2SnB8WQ/62l8L9aLxLuiRUTP4wE9tHkITeimgi0NBitTxhGgo6lgYgR+VcNDSGDSMj4ilkIp6mOvleNu2DqlGr1pqH5fpFHkcBbIMSqADHIM6uAIN0AI3INH8AxelAflSXlV3iatM0o+swV+SXn/Amoymio=</latexit> <latexit sha1_base64="C5mojRDEHRc/VWFZWbt3pwOTLkc=">ACEnicbVDLSsNAFJ34rPUVdelmsAjtpqRWfK2qblxWtA9oQplMJ+3QySTMTJQS+g1u/BU3LhRx68qdf+MkjaLWAxcO59zLvfe4IaNSWdaHMTM7N7+wmFvKL6+srq2bG5tNGUQCkwYOWCDaLpKEU4aipG2qEgyHcZabnD8Rv3RAhacCv1Sgkjo/6nHoUI6WlrlmyQ3pi+0gNMGLx1dgWtD9QSIjgFtZF8cuBp6WuWbDKVgo4TSoZKYAM9a75bvcCHPmEK8yQlJ2KFSonRkJRzMg4b0eShAgPUZ90NOXIJ9KJ05fGcFcrPegFQhdXMFV/TsTIl3Lku7ozOVH+9RLxP68TKe/IiSkPI0U4nizyIgZVAJN8YI8KghUbaYKwoPpWiAdIKx0ivk0hOMEB98vT5PmXrlSLVcv9wu1syOHNgGO6AIKuAQ1MAFqIMGwOAOPIAn8GzcG4/Gi/E6aZ0xspkt8AvG2ycfJ3b</latexit> <latexit sha1_base64="uXAjvt20+uKMGj8x1xAWtnNL+o=">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</latexit> Background • MDP h S , A , R, T, γ i • policy π : S → Pr ( A ) ∞ • return X G t := r t + γ r t +1 + γ 2 r t +2 + ... = γ i r t + i i =0 • Q function Q π ( s, a ) := E [ G t | s t = s, a t = a, π ] • optimal Q function Q ∗ ( s, a ) := max Q π ( s, a ) π learn from interaction. � 5

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend