  Designing reinforcement learning (RL) agents is typically a difficult process
that requires numerous design iterations. Learning can fail for a multitude of
reasons, and standard RL methods provide too few tools to provide insight into
the exact cause. In this paper, we show how to integrate value decomposition
into a broad class of actor-critic algorithms and use it to assist in the
iterative agent-design process. Value decomposition separates a reward function
into distinct components and learns value estimates for each. These value
estimates provide insight into an agent's learning and decision-making process
and enable new training methods to mitigate common problems. As a
demonstration, we introduce SAC-D, a variant of soft actor-critic (SAC) adapted
for value decomposition. SAC-D maintains similar performance to SAC, while
learning a larger set of value predictions. We also introduce
decomposition-based tools that exploit this information, including a new reward
influence metric, which measures each reward component's effect on agent
decision-making. Using these tools, we provide several demonstrations of
decomposition's use in identifying and addressing problems in the design of
both environments and agents. Value decomposition is broadly applicable and
easy to incorporate into existing algorithms and workflows, making it a
powerful tool in an RL practitioner's toolbox.
