Metadata-Version: 2.4
Name: AgenticLearnPro
Version: 0.1.2
Summary: A simple agentic AI package using reinforcement learning
Home-page: https://github.com/stephanieewelu/AgenticLearnPro
Author: Stephanie Ewelu
Author-email: stephanieewelu@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.19
Requires-Dist: gymnasium>=0.29
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Agentic AI

A simple agentic AI package using reinforcement learning. This package provides a basic implementation of a Q-learning agent that can interact with a simple environment to achieve goals.

## Installation

```bash
pip install AgenticLearnPro
```

## Usage

```python
from AgenticLearnPro.agent import QLearningAgent
from AgenticLearnPro.environment import SimpleEnv

# Create environment and agent
env = SimpleEnv()
agent = QLearningAgent(state_space=env.state_space, action_space=env.action_space)

# Train the agent
for episode in range(100):
    state = env.reset()
    done = False
    while not done:
        action = agent.choose_action(state)
        next_state, reward, done = env.step(action)
        agent.learn(state, action, reward, next_state)
        state = next_state
    agent.decay_exploration()

# Test the trained agent
state = env.reset()
done = False
total_reward = 0
while not done:
    action = agent.choose_action(state)
    next_state, reward, done = env.step(action)
    total_reward += reward
    state = next_state
    print(f"State: {state}, Action: {action}, Reward: {reward}")
print(f"Total reward: {total_reward}")
```

## Features

- Simple Q-learning agent implementation
- Basic environment with states and actions
- Configurable learning parameters
- Exploration rate decay for better convergence

## License

MIT

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
