To refer to this page use:
|Abstract:||The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learning setup with no prior information on the global state transition and local agent cost statistics. Specifically, with the agents’ objective consisting of minimizing a network-averaged infinite horizon discounted cost, the paper proposes a distributed version of Q-learning, QD-learning, in which the network agents collaborate by means of local processing and mutual information exchange over a sparse (possibly stochastic) communication network to achieve the network goal. Under the assumption that each agent is only aware of its local online cost data and the inter-agent communication network is weakly connected, the proposed distributed scheme is almost surely (a.s.) shown to yield asymptotically the desired value function and the optimal stationary control policy at each network agent. The analytical techniques developed in the paper to address the mixed time-scale stochastic dynamics of the consensus + innovations form, which arise as a result of the proposed interactive distributed scheme, are of independent interest.|
|Citation:||Kar, Soummya, Moura, José MF, Poor, H Vincent. (2013). QD-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations. IEEE Transactions on Signal Processing, 61 (7), 1848 - 1862. doi:10.1109/TSP.2013.2241057|
|Pages:||1848 - 1862|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||IEEE Transactions on Signal Processing|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.