Multi-agent Natural Actor-critic Reinforcement Learning Algorithms. (arXiv:2109.01654v2 [cs.LG] UPDATED)

Multi-agent actor-critic algorithms are an important part of the
Reinforcement Learning paradigm. We propose three fully decentralized
multi-agent natural actor-critic (MAN) algorithms in this work. The objective
is to collectively find a joint policy that maximizes the average long-term
return of these agents. In the absence of a central controller and to preserve
privacy, agents communicate some information to their neighbors via a
time-varying communication network. We prove convergence of all the 3 MAN
algorithms to a globally asymptotically stable …

algorithms arxiv learning natural reinforcement learning