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Documents/BasicConcepts.md

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@@ -18,7 +18,6 @@ This repo provides or plans to provide following tools for game AI and machine l
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4. Neural Evolution(work in progess)
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* Evolve the neural network's weights using MAES instead of gradien descent.
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### Other tools
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1. GAN(Generative adversarial network)
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* Including [Traning with Prediction to Stableize](https://www.semanticscholar.org/paper/Stabilizing-Adversarial-Nets-With-Prediction-Yadav-Shah/ec25504486d8751e00e613ca6fa64b256e3581c8).
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Assume that you are somehow familiar with Unity ML-Agents, then following will be some brief explanation of concepts/key conponents that are used in this repo.
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(To be added)
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* Trainer
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* Model
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* MEAS Optimizer
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* UnityNetwork
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* Agent Dependent Decision
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### Trainer
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The Brain in ML-Agent will communiate the with a Trainer to train the Model. We added a `CoreBrainInternalTrainable` on top of the existing core brains in ML-Agent which can communicate with our Trainers. The CoreBrainInternalTrainable works with any Monobehaviour that implement the `ITrainer` interface.
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We made some Trainers for you already for specific algorithm including PPO, SupervisedLearning and Evolution Strategy.
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### Model
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Models are the core of our AI. You can query information including the actions giving it the observations. Also, it provides interface to train the neural network.
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Trainers will ask for actions and other training related data from Models during the training, and also ask to train the neural network when enough data can be provided.
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### UnityNetwork
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We defined some UnityNetwork scriptable objects, where you can easily define a neural network architecture for different Models, and use them as plugin modules(thanks to Unity's Scriptable Object).
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The models implemented by us usually need a network scriptable object that implement certain interface. We have already made the simple version of those network for you. However, you can also easily make your own customized network.
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## Features Not Gonna Have
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1. Curriculum Training

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