structured computation and representation in deep
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Structured Computation and Representation in Deep Reinforcement - PowerPoint PPT Presentation

Sep 19, 2022 •117 likes •1.71k views

Structured Computation and Representation in Deep Reinforcement Learning Jessica B. Hamrick AAMAS/ICML/IJCAI Workshop on Prediction and Generative Modeling in RL Stockholm, Sweden July 15, 2018 Understanding the World in Terms of Objects and

  1. “Iterative” Controller Expert (IN, MLP, etc.) : Model of the world that evaluates proposed controls Memory (LSTM) : 
 Encodes the full history of controls and opinions Jessica Hamrick (@jhamrick) 21

  2. “Iterative” Controller Expert (IN, MLP, etc.) : Model of the world that evaluates proposed controls Memory (LSTM) : 
 Encodes the full history of controls and opinions Jessica Hamrick (@jhamrick) 21

  3. “Iterative” Controller Expert (IN, MLP, etc.) : Model of the world that evaluates proposed controls Memory (LSTM) : 
 Encodes the full history of controls and opinions Jessica Hamrick (@jhamrick) 21

  4. Iterative controller Jessica Hamrick (@jhamrick) 22

  5. Iterative controller Jessica Hamrick (@jhamrick) 22

  6. Iterative controller This is using a learned expert, which is trained simultaneously with the agent Jessica Hamrick (@jhamrick) 22

  7. Metacontroller Jessica Hamrick (@jhamrick) 23

  8. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Jessica Hamrick (@jhamrick) 23

  9. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  10. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  11. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  12. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  13. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  14. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  15. Metacontroller Manager (MLP) : Meta-level policy that determines whether to send the proposed control to the world, or to an expert, in order to minimize performance loss + resource loss Experts (IN, MLP, etc.) : Different models of the world, each with different resource costs Jessica Hamrick (@jhamrick) 23

  16. Metacontroller: simple task Jessica Hamrick (@jhamrick) 24

  17. Metacontroller: simple task Jessica Hamrick (@jhamrick) 24

  18. Metacontroller: hard task Jessica Hamrick (@jhamrick) 25

  19. Metacontroller: hard task Jessica Hamrick (@jhamrick) 25

  20. Imagination-Based Planner (Pascanu, Li, et al., 2017) Jessica Hamrick (@jhamrick) 26

  21. Imagination-Based Planner (Pascanu, Li, et al., 2017) Jessica Hamrick (@jhamrick) 26

  22. Takeaways Jessica Hamrick (@jhamrick) 27

  23. Takeaways 1. It can be useful to think about the choice of computation (which then allows e.g. choosing between multiple models). Jessica Hamrick (@jhamrick) 27

  24. Takeaways 1. It can be useful to think about the choice of computation (which then allows e.g. choosing between multiple models). 2. Enables a natural tradeoff between model-free and model- based computation by choosing the amount of computation being performed. Jessica Hamrick (@jhamrick) 27

  25. Takeaways 1. It can be useful to think about the choice of computation (which then allows e.g. choosing between multiple models). 2. Enables a natural tradeoff between model-free and model- based computation by choosing the amount of computation being performed. 3. Building in these choices is a type of structured computation that goes beyond the distinction of simply having a model or not having a model. Jessica Hamrick (@jhamrick) 27

  26. Outline 1. Structured Computation : the way that individual computations or functions are composed into more complex structures. Hamrick, Ballard, Pascanu, Vinyals, Heess, & Battaglia (2017, ICLR) 2. Structured Representation : the format of the data that computations are performed over. Hamrick*, Allen*, Bapst, Zhu, McKee, Tenenbaum, & Battaglia (2018, CogSci) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018, arXiv) Jessica Hamrick (@jhamrick) 28

  27. Outline 1. Structured Computation : the way that individual computations or functions are composed into more complex structures. Hamrick, Ballard, Pascanu, Vinyals, Heess, & Battaglia (2017, ICLR) 2. Structured Representation : the format of the data that computations are performed over. Hamrick*, Allen*, Bapst, Zhu, McKee, Tenenbaum, & Battaglia (2018, CogSci) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018, arXiv) Jessica Hamrick (@jhamrick) 28

  28. Structure in Deep Networks Sharing in space Sharing in time Fully-Connected Layer Convolutional Layer Recurrent Layer Unstructured computation Structured computation Structured computation Unstructured representation Unstructured representation Unstructured representation Jessica Hamrick (@jhamrick) 29

  29. Graph Networks Jessica Hamrick (@jhamrick) 30

  30. Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Jessica Hamrick (@jhamrick) 30

  31. Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) Jessica Hamrick (@jhamrick) 30

  32. Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) 1. Takes graphs as input, return graphs as output Jessica Hamrick (@jhamrick) 30

  33. Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) 1. Takes graphs as input, return graphs as output 2. Invariant to the permutation of the nodes and edges Jessica Hamrick (@jhamrick) 30

  34. Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) 1. Takes graphs as input, return graphs as output 2. Invariant to the permutation of the nodes and edges 3. Invariant to the number of nodes and edges Jessica Hamrick (@jhamrick) 30

  35. Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) Jessica Hamrick (@jhamrick) 31

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(2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) Edges Nodes Globals E V u Jessica Hamrick (@jhamrick) 31

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(2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) Edges Nodes Globals E V u Edge update e 0 e 0 j ! i u k v i e 0 i ! j = φ e ( v i , v j , e i ! j , u ) Jessica Hamrick (@jhamrick) 31

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(2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) Edges Nodes Globals E V u Edge update Node update e 0 e 0 e 0 e 0 j ! i v 0 j ! i u u k k v i i X v 0 e 0 e 0 i ! j = φ e ( v i , v j , e i ! j , u ) i = φ v ( v i , j ! i , u ) j Jessica Hamrick (@jhamrick) 31

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sha1_base64="eRAD8wnTQLVOsQuOEyaXMrO5E5Y=">AB8XicdVBNS8NAFHypX7V+VT16WSyCp5LU1vRY8NJjBVuLbSib7aZdutmE3Y1Qv+Fw+KePXfePfuEkrqOjAwjDzHjtv/JgzpW37wyqsrW9sbhW3Szu7e/sH5cOjnoSWiXRDySfR8rypmgXc0p/1YUhz6nN76s6vMv72nUrFI3Oh5TL0QTwQLGMHaSHfDEOupH6TJYlSu2NXapdu4sJEhbr3huDlp2m4dOVU7RwVW6IzK78NxRJKQCk04Vmrg2LH2Uiw1I5wuSsNE0RiTGZ7QgaECh1R5aZ54gc6MkZBJM0TGuXq940Uh0rNQ9MZgnVby8T/IGiQ6aXspEnGgqyPKjIOFIRyg7H42ZpETzuSGYSGayIjLFEhNtSiqZEr4uRf+TXq3q2FXnul5ptVd1FOETuEcHChBW3oQBcICHiAJ3i2lPVovVivy9GCtdo5h+w3j4BadCRbA=</latexit> <latexit sha1_base64="eRAD8wnTQLVOsQuOEyaXMrO5E5Y=">AB8XicdVBNS8NAFHypX7V+VT16WSyCp5LU1vRY8NJjBVuLbSib7aZdutmE3Y1Qv+Fw+KePXfePfuEkrqOjAwjDzHjtv/JgzpW37wyqsrW9sbhW3Szu7e/sH5cOjnoSWiXRDySfR8rypmgXc0p/1YUhz6nN76s6vMv72nUrFI3Oh5TL0QTwQLGMHaSHfDEOupH6TJYlSu2NXapdu4sJEhbr3huDlp2m4dOVU7RwVW6IzK78NxRJKQCk04Vmrg2LH2Uiw1I5wuSsNE0RiTGZ7QgaECh1R5aZ54gc6MkZBJM0TGuXq940Uh0rNQ9MZgnVby8T/IGiQ6aXspEnGgqyPKjIOFIRyg7H42ZpETzuSGYSGayIjLFEhNtSiqZEr4uRf+TXq3q2FXnul5ptVd1FOETuEcHChBW3oQBcICHiAJ3i2lPVovVivy9GCtdo5h+w3j4BadCRbA=</latexit> Graph Networks Gori et al. (2005), Scarselli et al. (2005), Scarselli et al. (2009), Li et al. (2015), Gilmer et al. (2017) Battaglia, Hamrick, Bapst, Sanchez-Gonzalez, Zambaldi, et al. (2018) Edges Nodes Globals E V u Edge update Node update Globals update e 0 e 0 e 0 e 0 e 0 e 0 u 0 j ! i v 0 j ! i v 0 j ! i u u k k k v i i i u 0 = φ u ( X X X v 0 e 0 v 0 e 0 e 0 i ! j = φ e ( v i , v j , e i ! j , u ) i = φ v ( v i , j ! i , u ) i ! j , u ) i , j i i,j Jessica Hamrick (@jhamrick) 31

  40. The Gluing Task Joint work with Kelsey Allen (MIT) Goal: glue blocks together to make the tower stable, using the minimum amount of glue. Jessica Hamrick (@jhamrick) 32

  41. The Gluing Task Joint work with Kelsey Allen (MIT) Goal: glue blocks together to make the tower stable, using the minimum amount of glue. Jessica Hamrick (@jhamrick) 32

  42. The Gluing Task Joint work with Kelsey Allen (MIT) Goal: glue blocks together to make the tower stable, using the minimum amount of glue. Jessica Hamrick (@jhamrick) 32

  43. The Gluing Task Joint work with Kelsey Allen (MIT) Goal: glue blocks together to make the tower stable, using the minimum amount of glue. Jessica Hamrick (@jhamrick) 32

  44. Jessica Hamrick (@jhamrick) 33

  45. Jessica Hamrick (@jhamrick) 33

  46. Learning a Policy Over the Edges of a Graph Jessica Hamrick (@jhamrick) 34

  47. Learning a Policy Over the Edges of a Graph Jessica Hamrick (@jhamrick) 34

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