Improve your Unity A.I. | Configuration

In this video I'm going to show you how you can improve your Unity AI. You will learn the ML-Agents configuration parameters for the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithm. Those are two commonly used state-of-the-art reinforcement learning algorithms.

Please let me know if you have any questions. I'll try my best to help you out.

PPO Paper:
arxiv.org/abs/1707.06347

SAC Paper:
arxiv.org/abs/1801.01290

IMPALA Resnet Paper:
arxiv.org/abs/1802.01561


Download my Unity A.I. example:
github.com/Bot-Academy/BallJump

Find me on:
Discord: discord.gg/6fRE4DE
Twitter: twitter.com/bot_academy
Instagram: www.instagram.com/therealbotacademy/
Patreon: www.patreon.com/botacademy

Credits:
11.55 - End
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Music: Ansia Orchestra - Hack The Planet
Link:    • Ansia Orchestra - Hack The Planet [Epic/Ci...  
Music provided by: MFY - No Copyright
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Contact: smarter.code.yt@gmail.com

Chapters:
00:00 Intro
00:17 PPO vs. SAC
03:09 Parameter: trainer
03:21 Parameter: summary_freq
03:57 Parameter: max_steps
04:39 Parameter: time_horizon
05:36 Parameter: init_path
06:41 Parameter: threaded
07:26 Parameter: summary_freq
07:26 Neural Networks in a Nutshell
08:01 Parameter: num_layers
08:20 Parameter: hidden_units
08:54 Parameter: vis_encoder_type
10:37 Parameter: normalize
11:26 Recommendations & Next Video
12:10 Outro