  This study proposes the use of a social learning method to estimate a global
state within a multi-agent off-policy actor-critic algorithm for reinforcement
learning (RL) operating in a partially observable environment. We assume that
the network of agents operates in a fully-decentralized manner, possessing the
capability to exchange variables with their immediate neighbors. The proposed
design methodology is supported by an analysis demonstrating that the
difference between final outcomes, obtained when the global state is fully
observed versus estimated through the social learning method, is
$\varepsilon$-bounded when an appropriate number of iterations of social
learning updates are implemented. Unlike many existing dec-POMDP-based RL
approaches, the proposed algorithm is suitable for model-free multi-agent
reinforcement learning as it does not require knowledge of a transition model.
Furthermore, experimental results illustrate the efficacy of the algorithm and
demonstrate its superiority over the current state-of-the-art methods.
