Neural Networks Made Easy (part 53): Reward Decomposition

Reward function value decomposition is a method used in reinforcement learning to decompose a composite reward into its individual components. This allows the Agent to learn an importance function for each reward component, allowing for better understanding of how different aspects influence the Agent’s behavior and how to optimize the model training. Additionally, it can help identify the causes of issues and effectively adjust the model architecture, training process or reward function.