Understanding advanced reinforcement learning algorithms is one part of the equation. This chapter addresses the practical considerations required to effectively implement, train, and refine these complex agents.

You will examine aspects like selecting appropriate neural network architectures, strategies for hyperparameter tuning, organizing code for RL projects, and utilizing established software frameworks. We will also touch upon distributed training approaches, methods for improving reproducibility, techniques for debugging agent behavior, and optimizing computational performance. The focus is on translating theoretical knowledge into functional and efficient reinforcement learning systems.