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dhyuti-n/
autorl-ppo-halfcheetah-v5-20260607-194357

Name: autorl-ppo-halfcheetah-v5-20260607-194357
Availability: InStock

stable-baselines3

reinforcement-learning

autorl

ppo

halfcheetah-v5

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PPO 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	1134.85 ± 175.97
Steps Trained	335,000
Algorithm	PPO
Environment	HalfCheetah-v5
Pushed At	2026-06-07 19:43:56 UTC
Repo	`dhyuti-n/autorl-ppo-halfcheetah-v5-20260607-194357`

Usage

python

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

# Load the winning model from HuggingFace
model_path = hf_hub_download(repo_id="dhyuti-n/autorl-ppo-halfcheetah-v5-20260607-194357", filename="model.zip")
model = PPO.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")