Playing against Nature: causal discovery for decision making under uncertainty
This work addresses decision-making challenges for agents in uncertain environments, but it is incremental as it builds on existing causal and reinforcement learning methods.
The paper tackles the problem of decision-making under uncertainty by learning an unknown causal mechanism through sequential interactions, proposing a procedure where an agent uses and updates beliefs about the environment. As a result, in a simple scenario, the model achieves performance similar to Q-learning while also acquiring a causal model of the environment.
We consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to the decision maker. We ask how a decision maker can learn about this causal mechanism through sequential decision making as well as using current causal knowledge inside each round in order to make better choices had she not considered causal knowledge and propose a decision making procedure in which an agent holds \textit{beliefs} about her environment which are used to make a choice and are updated using the observed outcome. As proof of concept, we present an implementation of this causal decision making model and apply it in a simple scenario. We show that the model achieves a performance similar to the classic Q-learning while it also acquires a causal model of the environment.