Getting Started

Note

You can try the following examples online using Google Colab notebook: RL Baselines zoo notebook

The hyperparameters for each environment are defined in hyperparameters/algo_name.yml.

If the environment exists in this file, then you can train an agent using:

python -m rl_zoo3.train --algo algo_name --env env_id

Or if you are in the RL Zoo3 folder:

python train.py --algo algo_name --env env_id

For example (with evaluation and checkpoints):

python -m rl_zoo3.train --algo ppo --env CartPole-v1 --eval-freq 10000 --save-freq 50000

If the trained agent exists, then you can see it in action using:

python -m rl_zoo3.enjoy --algo algo_name --env env_id

For example, enjoy A2C on Breakout during 5000 timesteps:

python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4 --folder rl-trained-agents/ -n 5000