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dhyuti-n/
autorl-sac-halfcheetah-v5-20260607-200936

Name: autorl-sac-halfcheetah-v5-20260607-200936
Availability: InStock

stable-baselines3

reinforcement-learning

autorl

sac

halfcheetah-v5

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SAC on HalfCheetah-v5

Trained automatically by AutoRL — a multi-agent RL training race powered by W&B, Weave, and OpenAI.

Performance

Metric	Value
Mean Return	1219.24 ± 604.28
Steps Trained	55,000
Algorithm	SAC
Environment	HalfCheetah-v5
Pushed At	2026-06-07 20:09:36 UTC
Repo	`dhyuti-n/autorl-sac-halfcheetah-v5-20260607-200936`

Usage

python

# pip install stable-baselines3 huggingface_hub gymnasium mujoco imageio
from huggingface_hub import hf_hub_download
from stable_baselines3 import SAC
import gymnasium as gym
import imageio

# Load the winning model from HuggingFace
model_path = hf_hub_download(repo_id="dhyuti-n/autorl-sac-halfcheetah-v5-20260607-200936", filename="model.zip")
model = SAC.load(model_path)

# Record a video rollout
env = gym.make("HalfCheetah-v5", render_mode="rgb_array")
obs, _ = env.reset(seed=0)
frames, done = [], False
while not done:
    frames.append(env.render())
    action, _ = model.predict(obs, deterministic=True)
    obs, _, terminated, truncated, _ = env.step(action)
    done = terminated or truncated
env.close()

imageio.mimsave("rollout.mp4", frames, fps=30)
print("✓ Saved rollout.mp4")