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

We are working with Ray 1.10.0

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

Set YOUR_ROOT in your_constants.py

Run demo/demo_your_rllib_env.py You should see your robots running around until they bump into a chicken.

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/

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 ray-project/ray#22976; see workaround instructions in demo/demo_after_training.py

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 ray-project/ray#22976; see workaround instructions in demo/demo_after_training.py

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