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Training a Humanoid AI Robot to Walk Using Proximal Policy Optimisation (PPO)

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29 Sep 2020CPOL4 min read
In this article in the series we start to focus on one particular, more complex environment that PyBullet makes available: Humanoid, in which we must train a human-like agent to walk on two legs.
Here we are using the Proximal Policy Optimisation (PPO) algorithm. We look at: the history of the humanoid environment for reinforcement learning, an introduction to Proximal Policy Optimisation (PPO), and the particular learning parameters that we override.



This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


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