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
Twitter: / bot_academy
Instagram: / therealbotacademy
Patreon: / botacademy
Credits:
11.55 - End
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Music: Ansia Orchestra - Hack The Planet
Link: • Ansia Orchestra - Hack...
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
26 июл 2024