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README.md

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+
+---
+tags:
+- reinforcement-learning
+- ppo
+- space-invaders
+- atari
+- ale
+- gymnasium
+---
+
+# 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
+```python
+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()