Downloads · 30 days
0
ppo-space-invaders-10M-expert
mohamednabil500/ppo-space-invaders-10M-expert
ppo-space-invaders-10M-expert is a reinforcement learning model from mohamednabil500. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
This is a Proximal Policy Optimization (PPO) agent trained on Atari Space Invaders (v5) using a vectorized environment setup.
Access
Public
Updated Dec 25, 2025
Repo size
7 MB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.pt6.8 MB · 96%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Dec 25, 2025
- Updated
- Dec 25, 2025
- SHA
- b32a4ddf
Model card
From the Hugging Face model README
PPO Expert Agent for ALE/SpaceInvaders-v5 (10M Steps)
This is a Proximal Policy Optimization (PPO) agent trained on Atari Space Invaders (v5) using a vectorized environment setup.
Training Details
- Algorithm: PPO
- Environment: ALE/SpaceInvaders-v5 (with sticky actions)
- Total Timesteps: 10,000,000
- Frame Stacking: 4 frames
- Terminal on Life Loss: True (during training)
Performance
- Peak Score observed: 615.0
- Average Reward (approx): ~300-450 range at 10M steps.
- Behavior: Learned to clear multiple waves, use shields for cover, and target the Mystery Ship.
Usage
import torch
# Assumes you have the ActorCritic class defined in your script
config = Config() # Using your existing Config class
model = ActorCritic(input_channels=4, action_dim=6) # Space Invaders has 6 actions
model.load_state_dict(torch.load('ppo_final_10M.pt', map_location='cpu'))
model.eval()
- Task
- Reinforcement Learning
- Created
- Dec 25, 2025
- Updated
- Dec 25, 2025