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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.


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This article is part of the series 'Teach a Robot to Walk Deep Reinforcement Learning View All


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

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