PPO agent for LunarLander-v3
This repository contains a Stable-Baselines3 PPO actor–critic agent trained on LunarLander-v3.
Evaluation
Deterministic evaluation over 100 fixed-seed episodes:
| Metric | Value |
|---|---|
| Mean reward | 280.66 |
| Standard deviation | 34.31 |
Course-style score (mean - std) |
246.34 |
| Episodes scoring at least 200 | 99.0% |
| Minimum reward | 4.31 |
| Maximum reward | 322.05 |
The candidate was compared with the previous Hub model on the same 100 fixed seeds. The selection metric was mean_reward and the observed improvement was +12.575.
Architecture
- Algorithm: PPO
- Policy: MLP actor–critic
- Actor hidden layers:
[128, 128] - Critic hidden layers:
[128, 128]
Replay
Replay seed: 42
Replay reward: 266.92
Load the model
from huggingface_hub import hf_hub_download
from stable_baselines3 import PPO
checkpoint = hf_hub_download(
repo_id="KaptainKris/HuggingFace_RL_Course",
filename="ppo-LunarLander-v3.zip",
)
model = PPO.load(checkpoint)
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from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="KaptainKris/HuggingFace_RL_Course", filename="{MODEL FILENAME}.zip", )