  This paper aims to balance performance and cost in a two-hop wireless
cooperative communication network where the source and relays have
contradictory optimization goals and make decisions in a distributed manner.
This differs from most existing works that have typically assumed that source
and relay nodes follow a schedule created implicitly by a central controller.
We propose that the relays form an alliance in an attempt to maximize the
benefit of relaying while the source aims to increase the channel capacity
cost-effectively. To this end, we establish the trade problem as a Stackelberg
game, and prove the existence of its equilibrium. Another important aspect is
that we use multi-agent reinforcement learning (MARL) to approach the
equilibrium in a situation where the instantaneous channel state information
(CSI) is unavailable, and the source and relays do not have knowledge of each
other's goal. A multi-agent deep deterministic policy gradient-based framework
is designed, where the relay alliance and the source act as agents. Experiments
demonstrate that the proposed method can obtain an acceptable performance that
is close to the game-theoretic equilibrium for all players under time-invariant
environments, which considerably outperforms its potential alternatives and is
only about 2.9% away from the optimal solution.
