Book Image

Generative AI with Python and TensorFlow 2

By : Joseph Babcock, Raghav Bali
4 (1)
Book Image

Generative AI with Python and TensorFlow 2

4 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.
Table of Contents (16 chapters)
14
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15
Index

Running GAIL on PyBullet Gym

For our code example in this chapter, we will train a virtual agent to navigate a simulated environment – in many RL papers, this environment is simulated using the Mujoco framework (http://www.mujoco.org/). Mujoco stands for Multi joint dynamics with contacts – it is a physics "engine" that allows you to create an artificial agent (such as a pendulum or bipedal humanoid), where a "reward" might be an ability to move through the simulated environment.

While it is a popular framework used for developing reinforcement learning benchmarks, such as by the research group OpenAI (see https://github.com/openai/baselines for some of these implementations), it is also closed source and requires a license for use. For our experiments, we will use PyBullet Gymperium (https://github.com/benelot/pybullet-gym), a drop-in replacement for Mujoco that allows us to run a physics simulator and import agents trained in Mujoco...