Compact Frequency Memory for Reinforcement Learning with Hidden States.

Date

2019-10-28

Author

Polat, Faruk
Cilden, Erkin

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Memory-based reinforcement learning approaches keep track of past experiences of the agent in environments with hidden states. This may require extensive use of memory that limits the practice of these methods in a real-life problem. The motivation behind this study is the observation that less frequent transitions provide more reliable information about the current state of the agent in ambiguous environments. In this work, a selective memory approach based on the frequencies of transitions is proposed to avoid keeping the transitions which are unrelated to the agent’s current state. Experiments show that the usage of a compact and selective memory may improve and speed up the learning process.

Subject Keywords

Reinforcement learning, Memory-based learning, Compact frequency memory

URI

https://hdl.handle.net/11511/57829

DOI

https://doi.org/10.1007/978-3-030-33792-6_26

Conference Name

PRIMA: International Conference on Principles and Practice of Multi-Agent Systems

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

F. Polat and E. Cilden, “Compact Frequency Memory for Reinforcement Learning with Hidden States.,” presented at the PRIMA: International Conference on Principles and Practice of Multi-Agent Systems, Turin, Italy, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57829.