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practical_rllib_tutorial

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Practical tutorial on RLlib for deep hierarchical multi-agent reinforcement learning

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RLlib documentation: https://docs.ray.io/en/latest/index.html

We are working with Ray 1.10.0

# Tutorial Steps
## 1. Installation
As described in the RLlib documentation https://docs.ray.io/en/master/rllib/index.html 
```
conda create -n rllib python=3.8
conda activate rllib
pip install "ray[rllib]" tensorflow torch
```
Also
```
pip install pygame  # so we can visualize what is going on, 2.1.2
```
## 2. Setup
Set `YOUR_ROOT` in `your_constants.py`

## 3. Test the Environment
Run `demo/demo_your_rllib_env.py`
You should see your robots running around until they bump into a chicken.

## 4. Train Your Agent
Run `your_rllib_train.py` 
(set `NUM_ITERATIONS` to 500 for about an hour of training to match the results in the blog post)

Results at `your_home_dir/ray_results/YourTrainer`

Go into directory
```
cd your_home_dir/ray_results/YourTrainer
cd YourTrainer_YourEnvironment_?????_00000_0_2022-MM-DD_SS-NN-NN  # fill in with what is there
```

Run TensorBoard to see the results
```
tensorboard --logdir ./  
```
then go to http://localhost:6006/ 
## 5. Load and Use the Learned Policy
Put in your run and checkpoint number in `demo/demo_after_training.py` and run it.

The learned policy doesn't work great. It's just an example, and I haven't played around with the hyperparemeters, but you can tune it for 
your needs. Hyperparameter tuning is what you spend most of your time on anyway, see
https://www.youtube.com/watch?v=yuTkgi7scKo&ab_channel=TheOnion for further understanding.

Note: there is currently a bug in RLlib as of Aug 19, 2022 
https://github.com/ray-project/ray/issues/22976; see
workaround instructions in `demo/demo_after_training.py`

## 6. Look at Your Learned Policy
Put in your run and checkpoint number in `demo/demo_look_at_policies.py` and run it.

Note: there is currently a bug in RLlib as of Aug 19, 2022 
https://github.com/ray-project/ray/issues/22976; see
workaround instructions in `demo/demo_after_training.py`

# Credits
Icons from publicdomainvectors.org https://publicdomainvectors.org/

robot https://publicdomainvectors.org/en/free-clipart/Yellow-robot/81372.html

chicken https://publicdomainvectors.org/en/free-clipart/Vector-illustration-of-cartoon-chicken-confused/24675.htm

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