Intervention Efficient Algorithm for Two-Stage Causal MDPs
This work addresses the challenge of efficient intervention selection in causal MDPs for learners in sequential decision-making settings, representing an incremental advancement over prior causal-bandit frameworks.
The paper tackles the problem of minimizing regret in two-stage causal Markov Decision Processes (MDPs) with parallel causal graphs, proposing an algorithm that uses convex optimization for exploration and achieves an instance-dependent regret bound, with experimental validation of theoretical results.
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards. In this setup, the learner's goal is to identify atomic interventions that lead to high rewards by intervening on variables at each state. Generalizing the recent causal-bandit framework, the current work develops (simple) regret minimization guarantees for two-stage causal MDPs, with parallel causal graph at each state. We propose an algorithm that achieves an instance dependent regret bound. A key feature of our algorithm is that it utilizes convex optimization to address the exploration problem. We identify classes of instances wherein our regret guarantee is essentially tight, and experimentally validate our theoretical results.