Create README.md

README.md

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+---
+tags:
+- ML-Agents-SnowballTarget
+- ppo
+- deep-reinforcement-learning
+- reinforcement-learning
+- ml-agents
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: ML-Agents-SnowballTarget
+      type: ML-Agents-SnowballTarget
+    metrics:
+    - type: mean_reward
+      value: 26.02 +/- 2.14
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **ML-Agents-SnowballTarget**
+
+This is a trained model of a **PPO** agent playing **ML-Agents-SnowballTarget** using Unity ML-Agents.
+
+## Usage
+
+Download model and play it:
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.base_env import ActionTuple
+import numpy as np
+
+# Load the environment
+env = UnityEnvironment(file_name="path/to/SnowballTarget")
+
+# Reset the environment
+env.reset()
+behavior_names = list(env.behavior_specs)
+spec = env.behavior_specs[behavior_names[0]]
+
+# Load your trained model
+# (You'll need to implement model loading based on your training framework)
+
+# Run the environment
+decision_steps, terminal_steps = env.get_steps(behavior_names[0])
+
+while True:
+    # Get observations
+    obs = decision_steps.obs[0]
+    
+    # Get action from your model
+    # action = your_model.predict(obs)
+    
+    # Step the environment
+    action_tuple = ActionTuple(discrete=np.array([[action]]))
+    env.set_actions(behavior_names[0], action_tuple)
+    env.step()
+    
+    decision_steps, terminal_steps = env.get_steps(behavior_names[0])
+    
+    if len(terminal_steps) > 0:
+        break
+
+env.close()