Resource Allocation Among Agents with MDP-Induced Preferences

Understand

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging.

• We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources.
• We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems.
• This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity.