My Agentic AI Developer Final Project

Author: Jay Prasad Majhi
Publication ID: no.reply.bjay

Abstract

This project demonstrates an agentic AI system capable of autonomous decision-making, task execution, and adaptive learning in a dynamic environment. The AI agent interacts with a simulated environment, learns from rewards, and improves its performance over multiple episodes. The project integrates open-source code, datasets, and frameworks to provide a reproducible and extensible solution for reinforcement learning and AI experimentation.

Methodology

Designed a reinforcement learning agent using Python.
Implemented decision-making logic with a Q-learning inspired approach.
The simulated environment includes multiple states and reward signals.
Performance is measured over multiple episodes to evaluate learning efficiency.

Code

GitHub Repository: https://github.com/thebijaay/Agentic-Ai

Key Files:

main.py – Run the AI agent

agent.py – Decision-making logic

environment.py – Simulated environment

utils.py – Helper functions

requirements.txt – Python dependencies

Datasets

OpenAI Gym Environments: https://gym.openai.com/envs/
Kaggle CartPole Dataset: https://www.kaggle.com/datasets/zalando-research/cartpole
Hugging Face Datasets: https://huggingface.co/datasets
TensorFlow Hub Models: https://tfhub.dev/s?deployment-format=lite

Notes: Include dataset size, format, and preprocessing steps. Check licenses and usage terms of datasets and models.

Conclusion

This project provides a practical example of an agentic AI system. It highlights how AI agents can learn from interactions, adapt to dynamic environments, and be extended with publicly available datasets and models. Future work includes integrating more complex environments and advanced reinforcement learning algorithms.