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Automated Parking Using RL, a Unity ML-Agents Tutorial.

Thomas Van Iseghem
144 subscribers
16K views 3 years ago
In this tutorial I will show you, how to use ML-Agents, and create an automated parking system using reinforcement learning. Intro: 0:00 Requirements: 1:03 Installing Unity / Opening the project: 1:36 Installing ML-Agents (Unity): 2:49 Installing Python / ML-Agents: 4:02 Installing PyTorch: 6:16 Verifying the install: 7:09 Taking a look at the environment: 7:46 Taking a look at the agent's code: 18:29 The hardest part, rewards: 21:17 Preparing the environment for training: 22:26 Creating a demo file: 24:13 Looking at the trainer config: 25:43 Training from the editor: 31:45 Spinning up Tensorboard: 33:23 Training from a build: 35:38 Checking statistics: 38:26 Deploying the model: 39:25 Github: https://github.com/VanIseghemThomas/A... My website: https://www.thomasvaniseghem.be/portf... Proximal Policy Optimisation: https://openai.com/blog/openai-baseli... ...more
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Intro

0:00

Requirements

1:03

Installing Unity / Opening the project

1:36

Installing ML-Agents (Unity)

2:49

Installing Python / ML-Agents

4:02

Installing PyTorch

6:16

Verifying the install

7:09

Taking a look at the environment

7:46

Taking a look at the agent's code

18:29

The hardest part, rewards

21:17

Preparing the environment for training

22:26

Creating a demo file

24:13

Looking at the trainer config

25:43

Training from the editor

31:45

Spinning up Tensorboard

33:23

Training from a build

35:38

Checking statistics

38:26

Deploying the model

39:25
Automated Parking Using RL, a Unity ML-Agents Tutorial.
402Likes
16,495Views
2022Feb 2
In this tutorial I will show you, how to use ML-Agents, and create an automated parking system using reinforcement learning. Intro: 0:00 Requirements: 1:03 Installing Unity / Opening the project: 1:36 Installing ML-Agents (Unity): 2:49 Installing Python / ML-Agents: 4:02 Installing PyTorch: 6:16 Verifying the install: 7:09 Taking a look at the environment: 7:46 Taking a look at the agent's code: 18:29 The hardest part, rewards: 21:17 Preparing the environment for training: 22:26 Creating a demo file: 24:13 Looking at the trainer config: 25:43 Training from the editor: 31:45 Spinning up Tensorboard: 33:23 Training from a build: 35:38 Checking statistics: 38:26 Deploying the model: 39:25 Github: https://github.com/VanIseghemThomas/A... My website: https://www.thomasvaniseghem.be/portf... Proximal Policy Optimisation: https://openai.com/blog/openai-baseli...

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Thomas Van Iseghem

144 subscribers