README.md

---
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()