article · KiltHub Repository · 2018

Self-improving reactive agents : case studies of reinforcement learning frameworks

Lin, Long-Ji

Cited by 0Open access

Abstract

Abstract: "The purpose of this work is to investigate and evaluate different reinforcement learning frameworks using connectionist networks. I study four frameworks, which are adopted from the ideas developed by Rich Sutton and his colleagues. The four frameworks are based on two learning procedures: the Temporal Difference methods for solving the credit assignment problem, and the backpropagation algorithm for developing appropriate internal representations. Two of them also involve learning a world model and using it to speed learning. To evaluate their performance, I design a dynamic environment and implement different learning agents, using the different frameworks, to survive in it. The environment is nontrivial and nondeterministic. Surprisingly, all of the agents can learn to survive fairly well in a reasonable time frame. This paper describes the learning agents and their performance, and summarizes the learning algorithms and the lessons I learned from this study."

Cite this paper

APA 7

Lin, L.-J. (2018). Self-improving reactive agents : case studies of reinforcement learning frameworks. KiltHub Repository. https://doi.org/10.1184/r1/6609398

MLA 9

Lin, Long-Ji. “Self-improving reactive agents : case studies of reinforcement learning frameworks.” KiltHub Repository, 2018, https://doi.org/10.1184/r1/6609398.

Chicago

Lin, Long-Ji. 2018. “Self-improving reactive agents : case studies of reinforcement learning frameworks.” KiltHub Repository. https://doi.org/10.1184/r1/6609398.

Harvard

Lin, L.-J. (2018) ‘Self-improving reactive agents : case studies of reinforcement learning frameworks’, KiltHub Repository. Available at: https://doi.org/10.1184/r1/6609398.

BibTeX

@article{lin2018self,
  title = {{Self-improving reactive agents : case studies of reinforcement learning frameworks}},
  author = {Lin, Long-Ji},
  journal = {KiltHub Repository},
  year = {2018},
  doi = {10.1184/r1/6609398}
}

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