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. 2022 Jul 20;6(1):166–188. doi: 10.5334/cpsy.83

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Copyright: © 2022 The Author(s)

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

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Schematic diagram illustrating the RL framework, showing that the agent receives information about the state they are in and the reward they have received from the environment, and adjusting their action selection policy based on this feedback — Schematic diagram illustrating the RL framework. At every time step, the agent receives information about the state they are in (i.e., a representation of their current environment, such as the speed and position of the car when driving) and the amount of reward they have received. Based on this feedback, the agent aims to adjust its action selection policy to maximise the amount of reward obtained in the future.