Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

3 Nov 2019Jun SunGang WangGeorgios B. GiannakisQinmin YangZaiyue Yang

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors... (read more)

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