Survey on Practical Reinforcement Learning : from Imitation Learning to Offline Reinforcement Learning 


Vol. 48,  No. 11, pp. 1405-1417, Nov.  2023
10.7840/kics.2023.48.11.1405


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  Abstract

The reinforcement learning paradigm has shifted from online to offline recently. Such a change is to overcome the impracticality of online reinforcement learning, which is limited to simulation-based game tasks (e.g., Go, Chess, Atari, and so on). This paper reviews an offline reinforcement learning approach that builds a policy by leveraging previously collected fixed datasets. To elaborate, we deal with the state-of-the-art offline reinforcement learning algorithms, which have been proposed to mitigate the distributional shift. Lastly, we discuss the open problems and limitations of current offline reinforcement learning.

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[IEEE Style]

D. Lee, C. Eom, S. Choi, S. Kim, M. Kwon, "Survey on Practical Reinforcement Learning : from Imitation Learning to Offline Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 11, pp. 1405-1417, 2023. DOI: 10.7840/kics.2023.48.11.1405.

[ACM Style]

Dongsu Lee, Chanin Eom, Sungwoo Choi, Sungkwan Kim, and Minhae Kwon. 2023. Survey on Practical Reinforcement Learning : from Imitation Learning to Offline Reinforcement Learning. The Journal of Korean Institute of Communications and Information Sciences, 48, 11, (2023), 1405-1417. DOI: 10.7840/kics.2023.48.11.1405.

[KICS Style]

Dongsu Lee, Chanin Eom, Sungwoo Choi, Sungkwan Kim, Minhae Kwon, "Survey on Practical Reinforcement Learning : from Imitation Learning to Offline Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 11, pp. 1405-1417, 11. 2023. (https://doi.org/10.7840/kics.2023.48.11.1405)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880