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README.md

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+---
+tags:
+- Pixelcopter-PLE-v0
+- reinforce
+- reinforcement-learning
+- custom-implementation
+- deep-rl-class
+model-index:
+- name: Reinforce-Pixelcopter-PLE-v0
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Pixelcopter-PLE-v0
+      type: Pixelcopter-PLE-v0
+    metrics:
+    - type: mean_reward
+      value: 20.30 +/- 16.10
+      name: mean_reward
+      verified: false
+---
+# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
+
+This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0**.
+
+Trained for Unit 4 of the Deep Reinforcement Learning Course:
+https://huggingface.co/deep-rl-course/unit4/introduction

hyperparameters.json

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+{
+  "h_size": 64,
+  "n_training_episodes": 10000,
+  "n_evaluation_episodes": 10,
+  "max_t": 10000,
+  "gamma": 0.99,
+  "lr": 0.0001,
+  "env_id": "Pixelcopter-PLE-v0",
+  "state_space": 7,
+  "action_space": 2
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9da8620cafe4017c752135c0ad820804ed9abc0f963663a5ee0783ff5386da6
+size 39389

replay.txt

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+Replay video was not generated for this run.
+Reason: RuntimeError("No frames were captured. Make sure the env was created with render_mode='rgb_array'.")
+Tip: If this is Pixelcopter, video capture may require a pygame display or PLE screen capture.

results.json

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+{
+  "env_id": "Pixelcopter-PLE-v0",
+  "mean_reward": 20.3,
+  "std_reward": 16.099999999999998,
+  "n_evaluation_episodes": 10,
+  "eval_datetime": "2026-01-07T00:20:08.452116"
+}