sac-seals-Ant-v0

SAC Agent playing seals/Ant-v0

This is a trained model of a SAC agent playing seals/Ant-v0 using the stable-baselines3 library and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo sac --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo sac --env seals/Ant-v0  -f logs/

If you installed the RL Zoo3 via pip (pip install rl_zoo3), from anywhere you can do:

python -m rl_zoo3.load_from_hub --algo sac --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo sac --env seals/Ant-v0  -f logs/

Training (with the RL Zoo)

python train.py --algo sac --env seals/Ant-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Ant-v0 -f logs/ -orga HumanCompatibleAI

Hyperparameters

OrderedDict([('batch_size', 512),
             ('buffer_size', 1000000),
             ('gamma', 0.98),
             ('learning_rate', 0.0018514039303149058),
             ('learning_starts', 1000),
             ('n_timesteps', 1000000.0),
             ('policy', 'MlpPolicy'),
             ('policy_kwargs',
              {'log_std_init': -2.2692589009754176,
               'net_arch': [256, 256],
               'use_sde': False}),
             ('tau', 0.05),
             ('train_freq', 64),
             ('normalize', False)])