Dynamic Programming Principles for Mean-Field Controls with Learning

Abstract

Dynamic programming principle (DPP) is fundamental for control and optimization, including Markov decision problems (MDPs), reinforcement learning (RL), and more recently mean-field controls (MFCs). However, in the learning framework of MFCs, DPP has not been rigorously established, despite its critical importance for algorithm designs. In this paper, we first present a simple example in MFCs with learning where DPP fails with a mis-specified Q function; and then propose the correct form of Q function in an appropriate space for MFCs with learning. This particular form of Q function is different from the classical one and is called the IQ function. In the special case when the transition probability and the reward are independent of the mean-field information, it integrates the classical Q function for single-agent RL over the state-action distribution. In other words, MFCs with learning can be viewed as lifting the classical RLs by replacing the state-action space with its probability distribution space. This identification of the IQ function enables us to establish precisely the DPP in the learning framework of MFCs. Finally, we illustrate through numerical experiments the time consistency of this IQ function.

References in corpus (4)

• Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning
• Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
• A Mean Field Game of Portfolio Trading and Its Consequences On Perceived Correlations
• Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis

Cited by in corpus (5)

• Game-Theoretic Multiagent Reinforcement Learning
• Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning
• Mean Field Markov Decision Processes
• Regularity and stability of feedback relaxed controls
• Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach