TQC Agent playing BipedalWalkerHardcore-v3
This is a trained model of a TQC agent playing BipedalWalkerHardcore-v3 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
Install the RL Zoo (with SB3 and SB3-Contrib):
pip install rl_zoo3
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env BipedalWalkerHardcore-v3 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo tqc --env BipedalWalkerHardcore-v3 -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 tqc --env BipedalWalkerHardcore-v3 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo tqc --env BipedalWalkerHardcore-v3 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo tqc --env BipedalWalkerHardcore-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env BipedalWalkerHardcore-v3 -f logs/ -orga MattStammers
Hyperparameters
OrderedDict([('batch_size', 256),
('buffer_size', 1000000),
('ent_coef', 'auto'),
('gamma', 0.99),
('gradient_steps', 1),
('learning_rate', 'lin_7.3e-4'),
('learning_starts', 10000),
('n_timesteps', 2000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('tau', 0.01),
('train_freq', 1),
('normalize', False)])
Environment Arguments
{'render_mode': 'rgb_array'}
This was my 6th attempt to crack this. I know it can be done with TD3 and SAC but not using sb3's basic implementation as I found out when I tried. Only the sb3 TQC implementation works