|
--- |
|
library_name: stable-baselines3 |
|
tags: |
|
- CarRacing-v0 |
|
- deep-reinforcement-learning |
|
- reinforcement-learning |
|
- stable-baselines3 |
|
model-index: |
|
- name: PPO |
|
results: |
|
- task: |
|
type: reinforcement-learning |
|
name: reinforcement-learning |
|
dataset: |
|
name: CarRacing-v0 |
|
type: CarRacing-v0 |
|
metrics: |
|
- type: mean_reward |
|
value: 174.99 +/- 100.17 |
|
name: mean_reward |
|
verified: false |
|
--- |
|
|
|
# **PPO** Agent playing **CarRacing-v0** |
|
This is a trained model of a **PPO** agent playing **CarRacing-v0** |
|
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) |
|
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-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): |
|
```bash |
|
pip install rl_zoo3 |
|
``` |
|
|
|
``` |
|
# Download model and save it into the logs/ folder |
|
python -m rl_zoo3.load_from_hub --algo ppo --env CarRacing-v0 -orga qgallouedec -f logs/ |
|
python -m rl_zoo3.enjoy --algo ppo --env CarRacing-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 ppo --env CarRacing-v0 -orga qgallouedec -f logs/ |
|
python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -f logs/ |
|
``` |
|
|
|
## Training (with the RL Zoo) |
|
``` |
|
python -m rl_zoo3.train --algo ppo --env CarRacing-v0 -f logs/ |
|
# Upload the model and generate video (when possible) |
|
python -m rl_zoo3.push_to_hub --algo ppo --env CarRacing-v0 -f logs/ -orga qgallouedec |
|
``` |
|
|
|
## Hyperparameters |
|
```python |
|
OrderedDict([('batch_size', 128), |
|
('clip_range', 0.2), |
|
('ent_coef', 0.0), |
|
('env_wrapper', |
|
[{'rl_zoo3.wrappers.FrameSkip': {'skip': 2}}, |
|
{'gym.wrappers.resize_observation.ResizeObservation': {'shape': 64}}, |
|
{'gym.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]), |
|
('frame_stack', 2), |
|
('gae_lambda', 0.95), |
|
('gamma', 0.99), |
|
('learning_rate', 'lin_1e-4'), |
|
('max_grad_norm', 0.5), |
|
('n_envs', 8), |
|
('n_epochs', 10), |
|
('n_steps', 512), |
|
('n_timesteps', 4000000.0), |
|
('normalize', "{'norm_obs': False, 'norm_reward': True}"), |
|
('policy', 'CnnPolicy'), |
|
('policy_kwargs', |
|
'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.GELU, ' |
|
'net_arch=dict(pi=[256], vf=[256]), )'), |
|
('sde_sample_freq', 4), |
|
('use_sde', True), |
|
('vf_coef', 0.5), |
|
('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})]) |
|
``` |
|
|
