metadata
library_name: stable-baselines3
tags:
- RocketLander-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- metrics:
- type: mean_reward
value: '-0.00 +/- 0.16'
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RocketLander-v0
type: RocketLander-v0
TQC Agent playing RocketLander-v0
This is a trained model of a TQC agent playing RocketLander-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
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo tqc --env RocketLander-v0 -orga araffin -f logs/
python enjoy.py --algo tqc --env RocketLander-v0 -f logs/
Training (with the RL Zoo)
python train.py --algo tqc --env RocketLander-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo tqc --env RocketLander-v0 -f logs/ -orga araffin
Hyperparameters
OrderedDict([('env_wrapper',
[{'utils.wrappers.FrameSkip': {'skip': 4}},
{'utils.wrappers.HistoryWrapper': {'horizon': 2}}]),
('n_timesteps', 3000000.0),
('policy', 'MlpPolicy'),
('normalize', False)])