https://github.com/marcosfede/reinforcement-landing

Reinforcement learning agent using Proximal Policy Optimization (PPO) and Unity
https://github.com/marcosfede/reinforcement-landing

deep-learning game-development machine-learning machine-learning-algorithms neural-networks proximal-policy-optimization python reinforcement-learning tensorflow unity

Last synced: about 2 months ago
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Reinforcement learning agent using Proximal Policy Optimization (PPO) and Unity

• Host: GitHub
• URL: https://github.com/marcosfede/reinforcement-landing
• Owner: marcosfede
• Created: 2018-01-29T19:54:52.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-01-26T13:37:10.000Z (almost 6 years ago)
• Last Synced: 2024-10-30T17:12:31.788Z (2 months ago)
• Topics: deep-learning, game-development, machine-learning, machine-learning-algorithms, neural-networks, proximal-policy-optimization, python, reinforcement-learning, tensorflow, unity
• Language: C#
• Size: 34.6 MB
• Stars: 9
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Reinforcement Learning project using PPO and Unity

## Get it running

- Install python deps

- You will need to install TensorflowSharp from [here](https://s3.amazonaws.com/unity-ml-agents/0.3/TFSharpPlugin.unitypackage)

- Set Brain type to external

- Build the game inside python dir

- Set up build name in ppo notebook

- run the code in the notebook

- Profit