We have seen in the previous chapters how states, actions, and rewards play a crucial role in driving an agent to reach its goal in a reinforcement learning (RL) environment. Now that we are familiar with the fundamentals of generic state-based RL, we will move ahead with the installation of all the libraries, frameworks, and extensions related to the usage of Unity ML agents as well as to further progress in the later modules of deep learning. Before we dive into the installation, let us try to understand the ML agents package made by Unity. Since its ...
© Abhilash Majumder 2021
A. MajumderDeep Reinforcement Learning in Unityhttps://doi.org/10.1007/978-1-4842-6503-1_33. Setting Up ML Agents Toolkit
Abhilash Majumder1
(1)
Pune, Maharashtra, India
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