Contributed to the pytorch/rl repository by implementing a feature that introduces per-head entropy coefficients mapping to the PPOLoss entropy regularization, allowing for granular control over exploration in multi-head policy networks. The work involved updating the loss function logic in Python and PyTorch to support configurable entropy coefficients for each policy head, enhancing experiment reproducibility and steerability. Comprehensive unit tests were added and updated to ensure correct application of coefficients across all heads, with validation integrated into the workflow. This targeted change supports faster experimentation and more precise tuning in reinforcement learning research, focusing on robust testing and maintainable code practices.