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Compact Frequency Memory for Reinforcement Learning with Hidden States.
Date
2019-10-28
Author
Polat, Faruk
Cilden, Erkin
Metadata
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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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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.