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training_tutorial notebook typo

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## Using ML-Agents and AnimalAI for your algorithms
As mentionned earlier AnimalAI is built on top of ML-Agents, and we strongly recommend you have a look at the various bits and pieces you can tinker with in order to implement your own agents. This part is a brief overview of where you can find these parts at the heart of most RL algortihms. We'll start from the higher level controllers down to the basic bricks of RL algorithms. Should you wish to modify them, you'll need to clone [ml-agents repository)(https://github.com/Unity-Technologies/ml-agents).
As mentionned earlier AnimalAI is built on top of ML-Agents, and we strongly recommend you have a look at the various bits and pieces you can tinker with in order to implement your own agents. This part is a brief overview of where you can find these parts at the heart of most RL algortihms. We'll start from the higher level controllers down to the basic bricks of RL algorithms. Should you wish to modify them, you'll need to clone [ml-agents repository](https://github.com/Unity-Technologies/ml-agents).
- `animalai_train.run_training`: contains the highest level of control for training an agent. You can find all the subroutines you need in order to do so. The most import ones are:
- `animalai_train.subprocess_env_manager_aai.SubprocessEnvManagerAAI`: an evnironment manager which derives from `mlagents.trainers.subprocess_env_manager.SubprocessEnvManager` and manages several environments to run in parallel. In prcatice you shouldn't need to change this part.
- `mlagents.trainers.trainer_util.TrainerFactory`: a factory method which is in charge of creating trainer methos to manage the agents in the environment. In practice we only have a single type of agents in every all the environments, therefore there will only be one trainer to manage all the agents. **You might need to change this code** in case you add a new RL algorithm as it is designed to handle PPO and SAC only.
- `animalai_train.trainer_controller_aai.TrainerControllerAAI`: derives from `mlagents.trainers.trainer_controller.TrainerController` and is where the training loop is.
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