IEEE Computational Intelligence Magazine - November 2023 - 64
Isaac Han , Seungwon Oh , Hoyoun Jung ,
Insik Chung , and Kyung-Joong Kim
Gwangju Institute ofScience and Technology, SOUTH KOREA
Monte Carlo and Temporal Difference Methods
in Reinforcement Learning
Abstract
R
einforcement learning (RL) is a
subset ofmachine learning that
allows intelligent agents to
acquire the ability of executing desired
actions through interactions with an environment.
Its remarkable progress has
achieved significant results in diverse
domains, such as Go and StarCraft, and
practical challenges like protein-folding.
This short paper presents overviews of
two common RL approaches: the Monte
Carlo and temporal difference methods.
To obtain a more comprehensive understanding
ofthese concepts and gain practical
experience, readers can access the full
article onIEEEXplore, whichincludes
interactive materials and examples.
I. Introduction
Reinforcement learning (RL) [1] is a
type of machine learning, in which an
agent learns to make decisions by trial and
error in a given environment. RL recently
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IEEE Computational Intelligence Magazine - November 2023
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