CausalXRL: Causal eXplanations in Reinforcement Learning

Deep reinforcement learning (RL) systems are approaching or surpassing human-level performance in specific domains, from games to decision support to continuous control, albeit in non-critical environments, and usually learning via random explorations. Despite these prodigious achievements, many applications cannot be considered today because we need to understand and explain how these AI systems make their decisions before letting them interact with, and possibly impact on, human beings and society.

There are two main obstacles for AI agents to explain their decisions: they have to be able to provide it at a level human beings can understand, and they have to deal with causal relations rather than statistical correlations. Hence, we believe the key to explainable AI, in particular for decision support, is to build or learn causal models of the system being intervened upon. Thus, instead of standard machine learning and reinforcement learning networks, we will leverage the new science of causal inference to equip deep RL systems with the ability to learn, plan with, and justifiably explore cause-effect relationships in their environment. RL systems based on this novel CausalXRL architecture will provide cause-effect and counterfactual justifications for their suggested actions, allowing them to fulfill the right to an explanation in human-centric environments.

We will implement the CausalXRL architecture as a bio-plausible (neuromorphic) algorithm to enable its deployment in resource-limited, e.g., mobile, environments. We will demonstrate the broad applicability and impact of CausalXRL on several use cases, ranging from neuro-rehabilitation to intensive care, farming and education.