Developed the initial groundwork for a reinforcement learning project in the OpenHUTB/nn repository, focusing on the Bipedal Walker environment using the Proximal Policy Optimization (PPO) algorithm. Established a modular Python codebase that standardized module naming conventions and improved overall project structure, facilitating easier onboarding and future development. Prioritized code quality and maintainability over bug fixing during this period, enabling faster iteration on reinforcement learning experiments. Collaborated through Git to ensure clear contributor guidelines and streamlined testing processes. The work leveraged Python, reinforcement learning techniques, and modular architecture to lay a solid foundation for ongoing machine learning research and experimentation.