43 lines
907 B
Markdown
43 lines
907 B
Markdown
# DeepAgent
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DeepAgent is a minimal example of a deep learning based search agent.
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It demonstrates how to combine a neural network with a simple search algorithm
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(Monte‑Carlo Tree Search style) without relying on external search libraries.
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## Installation
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```bash
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# Create a virtual environment (recommended)
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python -m venv .venv
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source .venv/bin/activate # On Windows use `.venv\\Scripts\\activate`
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# Install the package
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pip install .
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```
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## Usage
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```python
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from src.search_agent import SearchAgent, PolicyValueNet
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# Create a policy‑value network
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net = PolicyValueNet(input_dim=1, action_space=2)
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# Create the agent
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agent = SearchAgent(policy_value_net=net, max_depth=3)
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# Run the agent on a simple state
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state = 0
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action = agent.act(state)
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print(f"Chosen action: {action}")
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```
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## Running Tests
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```bash
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pytest
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```
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## License
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MIT License – see the [LICENSE](LICENSE) file for details. |