# DeepAgent

DeepAgent is a minimal example of a deep learning based search agent.  
It demonstrates how to combine a neural network with a simple search algorithm
(Monte‑Carlo Tree Search style) without relying on external search libraries.

## Installation

```bash
# Create a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows use `.venv\\Scripts\\activate`

# Install the package
pip install .
```

## Usage

```python
from src.search_agent import SearchAgent, PolicyValueNet

# Create a policy‑value network
net = PolicyValueNet(input_dim=1, action_space=2)

# Create the agent
agent = SearchAgent(policy_value_net=net, max_depth=3)

# Run the agent on a simple state
state = 0
action = agent.act(state)
print(f"Chosen action: {action}")
```

## Running Tests

```bash
pytest
```

## License

MIT License – see the [LICENSE](LICENSE) file for details.