Exploring the Frontiers of Reinforcement Learning
As an AI enthusiast, I have dedicated the past few months to diving deep into Reinforcement Learning (RL), implementing algorithms like DQN and Policy Gradient (REINFORCE) across multiple Atari games. Here’s what I’ve achieved:
- Hands-On Implementation: Developed RL solutions manually, gaining insights into their mechanics.
- Benchmarking with Stable Baselines: Utilized standardized tools for accurate performance evaluation.
- Future Endeavors: Planning to broaden the scope by integrating more games and algorithms into a unified RL model.
In addition, I am venturing into board games, exploring options for creating customized agents. Here’s what’s coming:
- Innovative Techniques: Combining planning algorithms like Monte Carlo Tree Search with player behavior cloning.
- Engineering Challenges: Developing smart storage solutions for managing extensive game data.
Stay tuned for exciting updates! Explore my journey on GitHub and share your thoughts below!
