TQC Agent playing PandaPickAndPlace-v3
This is a trained model of a TQC agent playing PandaPickAndPlace-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 SB3: https://github.com/DLR-RM/stable-baselines3 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 PandaPickAndPlace-v3 -orga chencliu -f logs/
python -m rl_zoo3.enjoy --algo tqc --env PandaPickAndPlace-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 PandaPickAndPlace-v3 -orga chencliu -f logs/
python -m rl_zoo3.enjoy --algo tqc --env PandaPickAndPlace-v3 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo tqc --env PandaPickAndPlace-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env PandaPickAndPlace-v3 -f logs/ -orga chencliu
Hyperparameters
OrderedDict([('batch_size', 2048),
('buffer_size', 1000000),
('ent_coef', 'auto'),
('gamma', 0.95),
('learning_rate', 0.001),
('learning_starts', 100),
('n_timesteps', 5000000.0),
('normalize', True),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( goal_selection_strategy='future', n_sampled_goal=4 )"),
('tau', 0.05),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
Environment Arguments
{'render_mode': 'rgb_array'}
Panda Gym environments: arxiv.org/abs/2106.13687