SAC HalfCheetah-v5
This is a Stable Baselines3 SAC policy trained locally on Gymnasium HalfCheetah-v5.
The environment uses a small anti-flip reward guard. It penalizes extreme torso plane angle and low torso height, then terminates clear fall or belly-slide exploit postures.
Files
sac_half_cheetah.zip: Stable Baselines3 SAC checkpoint.videos/initial.mp4: random policy before training.videos/final.mp4: trained policy rollout.sac_cheetah/: minimal training, environment, check, and video code.pyproject.toml: Python dependency setup.
Training
- Algorithm: Soft Actor-Critic
- Implementation: Stable Baselines3
- Policy:
MlpPolicy - Environment:
HalfCheetah-v5 - Timesteps:
300000 - Seed:
7 - Device: Quadro P520 with
torch==2.7.1+cu118
Evaluation
Single deterministic rollout with seed 8.
| Metric | Value |
|---|---|
| Steps | 1000 |
| Return | 7031.927 |
| Mean reward | 7.032 |
| Mean x velocity | 7.465 |
| Final x position | 373.190 |
| Minimum torso height | 0.534 |
| Maximum absolute root angle | 0.269 |
| Fell | false |
These numbers are a local smoke evaluation, not a benchmark sweep.
Load
from stable_baselines3 import SAC
model = SAC.load("sac_half_cheetah.zip", device="auto")
Use the wrapper in sac_cheetah.envs if you want evaluation to match this model card.
from sac_cheetah.config import TrainConfig
from sac_cheetah.envs import make_env
cfg = TrainConfig()
env = make_env(cfg.env_id, cfg.seed + 1, render_mode="rgb_array")
Limitations
This policy is only tested on Gymnasium HalfCheetah-v5 with the included anti-flip wrapper. It is not meant for real robots, safety-critical systems, or transfer to other MuJoCo tasks without retraining.
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