JACIII Vol.26 No.4 pp. 562-569
doi: 10.20965/jaciii.2022.p0562


Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing

Kodai Yamashita* and Tomoki Hamagami**

*Graduate School of Engineering Science, Yokohama National University
79-5 Tokiwadai, Hodogaya-ku, Yokohama-shi, Kanagawa 240-8501, Japan

**Faculty of Engineering, Yokohama National University
79-5 Tokiwadai, Hodogaya-ku, Yokohama-shi, Kanagawa 240-8501, Japan

December 18, 2021
April 12, 2022
July 20, 2022
reinforcement learning, reservoir computing, partially observable Markov decision process environment

One of the challenges in reinforcement learning is regarding the partially observable Markov decision process (POMDP). In this case, an agent cannot observe the true state of the environment and perceive different states to be the same. Our proposed method uses the agent’s time-series information to deal with this imperfect perception problem. In particular, the proposed method uses reservoir computing to transform the time-series of observation information into a non-linear state. A typical model of reservoir computing, the echo state network (ESN), transforms raw observations into reservoir states. The proposed method is named dual ESNs reinforcement learning, which uses two ESNs specialized for observation and action information. The experimental results show the effectiveness of the proposed method in environments where imperfect perception problems occur.

Dual ESNs RL using two reservoir layers

Dual ESNs RL using two reservoir layers

Cite this article as:
K. Yamashita and T. Hamagami, “Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.4, pp. 562-569, 2022.
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