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