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Merge pull request #523 from bpugnaire/fix-unit7-macsilicon
Fix issue #518
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@@ -78,6 +78,12 @@ pip install -e ./ml-agents-envs
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pip install -e ./ml-agents
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```
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Mac users on Apple Silicon may encounter troubles with the installation (e.g. ONNX wheel build failing), you should first try to install grpcio:
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```bash
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conda install grpcio
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```
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[This github issue](https://github.com/Unity-Technologies/ml-agents/issues/6019) in the official ml-agent repo might also help you.
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Finally, you need to install git-lfs: https://git-lfs.com/
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Now that it’s installed, we need to add the environment training executable. Based on your operating system you need to download one of them, unzip it and place it in a new folder inside `ml-agents` that you call `training-envs-executables`
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@@ -221,10 +227,16 @@ Depending on your hardware, 5M timesteps (the recommended value, but you can als
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Depending on the executable you use (windows, ubuntu, mac) the training command will look like this (your executable path can be different so don’t hesitate to check before running).
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For Windows, it might look like this:
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```bash
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mlagents-learn ./config/poca/SoccerTwos.yaml --env=./training-envs-executables/SoccerTwos.exe --run-id="SoccerTwos" --no-graphics
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```
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For Mac, it might look like this:
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```bash
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mlagents-learn ./config/poca/SoccerTwos.yaml --env=./training-envs-executables/SoccerTwos/SoccerTwos.app --run-id="SoccerTwos" --no-graphics
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```
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The executable contains 8 copies of SoccerTwos.
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⚠️ It’s normal if you don’t see a big increase of ELO score (and even a decrease below 1200) before 2M timesteps, since your agents will spend most of their time moving randomly on the field before being able to goal.
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