Enjoy a Trained Agent

Note

To download the repo with the trained agents, you must use git clone --recursive https://github.com/DLR-RM/rl-baselines3-zoo in order to clone the submodule too.

Enjoy a trained agent

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

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

For example, enjoy A2C on Breakout during 5000 timesteps:

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

If you have trained an agent yourself, you need to do:

# exp-id 0 corresponds to the last experiment, otherwise, you can specify another ID
python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 0

Load Checkpoints, Best Model

To load the best model (when using evaluation environment):

python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-best

To load a checkpoint (here the checkpoint name is rl_model_10000_steps.zip):

python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-checkpoint 10000

To load the latest checkpoint:

python enjoy.py --algo algo_name --env env_id -f logs/ --exp-id 1 --load-last-checkpoint

Record a Video of a Trained Agent

Record 1000 steps with the latest saved model:

python -m rl_zoo3.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000

Use the best saved model instead:

python -m rl_zoo3.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-best

Record a video of a checkpoint saved during training (here the checkpoint name is rl_model_10000_steps.zip):

python -m rl_zoo3.record_video --algo ppo --env BipedalWalkerHardcore-v3 -n 1000 --load-checkpoint 10000

Record a Video of a Training Experiment

Apart from recording videos of specific saved models, it is also possible to record a video of a training experiment where checkpoints have been saved.

Record 1000 steps for each checkpoint, latest and best saved models:

python -m rl_zoo3.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic

The previous command will create a mp4 file. To convert this file to gif format as well:

python -m rl_zoo3.record_training --algo ppo --env CartPole-v1 -n 1000 -f logs --deterministic --gif