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--- |
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tags: |
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- Pixelcopter-PLE-v0 |
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- reinforce |
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- reinforcement-learning |
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- custom-implementation |
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- deep-rl-class |
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model-index: |
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- name: Pixelcopter-RL |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: Pixelcopter-PLE-v0 |
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type: Pixelcopter-PLE-v0 |
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metrics: |
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- type: mean_reward |
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value: 13.10 +/- 6.89 |
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name: mean_reward |
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verified: false |
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--- |
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# REINFORCE Agent for Pixelcopter-PLE-v0 |
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## Model Description |
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This repository contains a trained REINFORCE (Policy Gradient) reinforcement learning agent that has learned to play Pixelcopter-PLE-v0, a challenging helicopter navigation game from the PyGame Learning Environment (PLE). The agent uses policy gradient methods to learn optimal flight control strategies through trial and error. |
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### Model Details |
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- **Algorithm**: REINFORCE (Monte Carlo Policy Gradient) |
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- **Environment**: Pixelcopter-PLE-v0 (PyGame Learning Environment) |
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- **Framework**: Custom implementation following Deep RL Course guidelines |
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- **Task Type**: Discrete Control (Binary Actions) |
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- **Action Space**: Discrete (2 actions: do nothing or thrust up) |
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- **Observation Space**: Visual/pixel-based or feature-based state representation |
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### Environment Overview |
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Pixelcopter-PLE-v0 is a classic helicopter control game where: |
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- **Objective**: Navigate a helicopter through obstacles without crashing |
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- **Challenge**: Requires precise timing and control to avoid ceiling, floor, and obstacles |
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- **Physics**: Gravity constantly pulls the helicopter down; player must apply thrust to maintain altitude |
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- **Scoring**: Points are awarded for surviving longer and successfully navigating through gaps |
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- **Difficulty**: Requires learning temporal dependencies and precise action timing |
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## Performance |
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The trained REINFORCE agent achieves the following performance metrics: |
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- **Mean Reward**: 13.10 ± 6.89 |
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- **Performance Analysis**: This represents solid performance for this challenging environment |
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- **Consistency**: The standard deviation indicates moderate variability, which is expected for policy gradient methods |
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## Educational Resources |
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This model was developed following the **Deep Reinforcement Learning Course Unit 4**: |
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- **Course Link**: [https://huggingface.co/deep-rl-course/unit4/introduction](https://huggingface.co/deep-rl-course/unit4/introduction) |
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- **Topic**: Policy Gradient Methods and REINFORCE |
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- **Learning Objectives**: Understanding policy-based RL algorithms |
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For comprehensive learning about REINFORCE and policy gradient methods, refer to the complete course materials. |
