Status: Active (under active development, breaking changes may occur)

This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).

For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.

This module contains a variety of helpful resources, including:

• a short introduction to RL terminology, kinds of algorithms, and basic theory,
• an essay about how to grow into an RL research role,
• a curated list of important papers organized by topic,
• a well-documented code repo of short, standalone implementations of key algorithms,
• and a few exercises to serve as warm-ups.

Get started at spinningup.openai.com!

The command for the run is python3 spinup/algos/sac/sac.py "192.168.1.14" "192.168.1.11" "192.168.1.10" "192.168.1.12" "192.168.1.23" --gamma 0.98 --start_steps 160000 --seed 2 --replay_size 4000000 --exp_name "TD 5 learning while sampling with evaluation in parallel gamma 098" --evaluate True

The IP addresses should be rewritten accordingly. In this case, 4 PS4s ("192.168.1.14" "192.168.1.11" "192.168.1.10" "192.168.1.12") are used for training and the last one "192.168.1.23" is used for evaluation. In your case, with only one PlayStation, you need to set "--evaluate False" and concentrate on the training only. You can evaluate the trained models later.

And if I remember correctly, scikit-learn (0.23.1) didn't work with the code for some backward incompatibilities. So I needed to use the older version by fixing the version of scikit-learn to 0.22.2 by: pip3 install scikit-learn==0.22.2