Initial commit — Elden Ring RL agent

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

train.py

@@ -0,0 +1,70 @@
+import torch
+from stable_baselines3 import PPO
+from elden_env import EldenRingEnv
+
+
+def get_system_info():
+    with open('/proc/meminfo') as f:
+        for line in f:
+            if line.startswith('MemTotal'):
+                ram_gb = int(line.split()[1]) / (1024 ** 2)
+                break
+
+    if torch.cuda.is_available():
+        props = torch.cuda.get_device_properties(0)
+        vram_gb = props.total_memory / (1024 ** 3)
+        device = "cuda"
+        gpu_name = props.name
+    else:
+        vram_gb = 0
+        device = "cpu"
+        gpu_name = "None"
+
+    return ram_gb, vram_gb, device, gpu_name
+
+
+def get_hyperparams(ram_gb, vram_gb):
+    # n_steps: rollout buffer size, lives in RAM.
+    # Each obs is (640,640,12) float32 = ~18.75MB. Use at most 20% of RAM.
+    if ram_gb >= 64:
+        n_steps = 2048
+    elif ram_gb >= 32:
+        n_steps = 512
+    elif ram_gb >= 16:
+        n_steps = 256
+    else:
+        n_steps = 128
+
+    # batch_size: minibatch for the gradient update, lives in VRAM.
+    if vram_gb >= 16:
+        batch_size = 128
+    elif vram_gb >= 8:
+        batch_size = 32
+    elif vram_gb >= 4:
+        batch_size = 16
+    else:
+        batch_size = 8
+
+    return n_steps, batch_size
+
+
+def train():
+    ram_gb, vram_gb, device, gpu_name = get_system_info()
+    n_steps, batch_size = get_hyperparams(ram_gb, vram_gb)
+
+    print(f"[HW] RAM: {ram_gb:.1f} GB | VRAM: {vram_gb:.1f} GB ({gpu_name}) | Device: {device}")
+    print(f"[HW] n_steps={n_steps}, batch_size={batch_size}")
+
+    env = EldenRingEnv()
+    model = PPO("CnnPolicy", env, verbose=1,
+                device=device,
+                n_steps=n_steps,
+                batch_size=batch_size)
+
+    print("Starting Training...")
+    model.learn(total_timesteps=100000)
+    model.save("elden_ai_model")
+
+
+if __name__ == "__main__":
+    train()