We provide examples to train the following algorithms:
To run these examples, make sure you have installed hydra:
pip install hydra-core
Scripts can be run from the directory of interest using:
python sac.py
or similar. Hyperparameters can be easily changed by providing the arguments to hydra:
python sac.py frames_per_batch=63
Here we can see some results for the SAC and REDQ algorithm. We average the results over 5 different seeds and plot the standard error.
To reproduce a single run:python sac/sac.py env_name="HalfCheetah-v4" env_task="" env_library="gym"
python redq/redq.py env_name="HalfCheetah-v4" env_task="" env_library="gym"
python sac/sac.py env_name="cheetah" env_task="run" env_library="dm_control"
python redq/redq.py env_name="cheetah" env_task="run" env_library="dm_control"
python sac/sac.py env_name="Ant-v4" env_task="" env_library="gym"
python redq/redq.py env_name="Ant-v4" env_task="" env_library="gym"
python sac/sac.py env_name="Walker2D-v4" env_task="" env_library="gym"
python redq/redq.py env_name="Walker2D-v4" env_task="" env_library="gym"