  Broad Explainable Artificial Intelligence moves away from interpreting
individual decisions based on a single datum and aims to provide integrated
explanations from multiple machine learning algorithms into a coherent
explanation of an agent's behaviour that is aligned to the communication needs
of the explainee. Reinforcement Learning (RL) methods, we propose, provide a
potential backbone for the cognitive model required for the development of
Broad-XAI. RL represents a suite of approaches that have had increasing success
in solving a range of sequential decision-making problems. However, these
algorithms all operate as black-box problem solvers, where they obfuscate their
decision-making policy through a complex array of values and functions.
EXplainable RL (XRL) is relatively recent field of research that aims to
develop techniques to extract concepts from the agent's: perception of the
environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and
objectives. This paper aims to introduce a conceptual framework, called the
Causal XRL Framework (CXF), that unifies the current XRL research and uses RL
as a backbone to the development of Broad-XAI. Additionally, we recognise that
RL methods have the ability to incorporate a range of technologies to allow
agents to adapt to their environment. CXF is designed for the incorporation of
many standard RL extensions and integrated with external ontologies and
communication facilities so that the agent can answer questions that explain
outcomes and justify its decisions.
