README.md

---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: REINFORCE-PixelCopter
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: Pixelcopter-PLE-v0
      type: Pixelcopter-PLE-v0
    metrics:
    - type: mean_reward
      value: 6.50 +/- 5.99
      name: mean_reward
      verified: false
---

  # **Reinforce** Agent playing **Pixelcopter-PLE-v0**
  This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
  Portable weights: `model_state_dict.pt`. Define the matching Unit 4 Policy
  (CartPole: two layers; PixelCopter: three layers), instantiate it with
  `state_space`, `action_space`, and `h_size` from `hyperparameters.json`, then
  use `policy.load_state_dict(torch.load(weights_path, map_location="cpu", weights_only=True))`.
  The legacy `model.pt` contains a full Python model and requires its matching class.
  To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction