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To support the newly introduced multimedia services with ultra-low latencyand extensive computation requirements, resource-constrained end user devicesshould utilize the ubiquitous computing resources available at network edge foraugmenting on-board (local) processing with edge computing. In this regard, thecapability of cell-free massive MIMO to provide reliable access links byguaranteeing uniform quality of service without cell edge can be exploited forseamless parallel processing. Taking this into account, we consider a cell-freemassive MIMO-enabled mobile edge network to meet the stringent requirements ofthe advanced services. For the considered mobile edge network, we formulate ajoint communication and computing resource allocation (JCCRA) problem with theobjective of minimizing energy consumption of the users while meeting the tightdelay constraints. We then propose a fully distributed cooperative solutionapproach based on multiagent deep deterministic policy gradient (MADDPG)algorithm. The simulation results demonstrate that the performance of theproposed distributed approach has converged to that of a centralized deepdeterministic policy gradient (DDPG)-based target benchmark, while alleviatingthe large overhead associated with the latter. Furthermore, it has been shownthat our approach significantly outperforms heuristic baselines in terms ofenergy efficiency, roughly up to 5 times less total energy consumption.

Multi-Agent Reinforcement Learning for Distributed Joint Communication  and Computing Resource Allocation over Cell-Free Massive MIMO-enabled Mobile  Edge Computing Network