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