A Review of Partially Observable Markov Decision Processes for Causal Modeling

Partially Observable Markov Decison Processes (POMDPs) are a framework for modeling sequential decision-making problems. At every time-step, an agent takes an action that causes some (hidden) state of the world to change. The hidden state then emits some observations and rewards that the agent may use to guide its next decision. In this way, POMDPs provide a useful model for actions that may have long-range temporal effects. For example, when entering a building, an initial decision to turn left or right will place an agent in very different places, even if all the agent's future actions are to continue forward.