Partially observable Markov decision processes are a generalization of MDPs that allow for uncertainty in the agent's observations. They model an agent's decision process where the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a sensor model and the underlying MDP to determine the optimal action.
Stanford University
Winter 2023
The course introduces decision making under uncertainty from a computational perspective, covering dynamic programming, reinforcement learning, and more. Prerequisites include basic probability and fluency in a high-level programming language.
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