KaptainKris/HuggingFace_RL_Course

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)