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Delta Schema Network in Model-Based Reinforcement Learning

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12177)

Abstract

This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in the area of reinforcement learning is a model-based approach. In the paper we are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data. We present algorithms for training a Delta Schema Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward. DSN shows strong performance of transfer learning on the classic Atari game environment.

Keywords

  • Reinforcement learning
  • Model-based
  • Schema Network
  • Delta Schema Network
  • Transfer learning

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Acknowledgements

The reported study was partially supported by RFBR, research Projects No. 17-29-07079 and No. 18-29-22027.

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Correspondence to Aleksandr I. Panov .

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A Appendix

A Appendix

Algorithms 1 and 2 are used in Algorithm 3. Node in the graph is considered to have next attributes:

figure a
figure b
figure c
  • node.is_reachable - the actual reachability of the node, subject to currently selected actions, or \(\texttt {None}\) if the reachability is not known.

  • node.schemas - map from actions to node’s schemas requiring these actions

  • node.transition - self-transition node, if any

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Gorodetskiy, A., Shlychkova, A., Panov, A.I. (2020). Delta Schema Network in Model-Based Reinforcement Learning. In: Goertzel, B., Panov, A., Potapov, A., Yampolskiy, R. (eds) Artificial General Intelligence. AGI 2020. Lecture Notes in Computer Science(), vol 12177. Springer, Cham. https://doi.org/10.1007/978-3-030-52152-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-52152-3_18

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