Collaborative deep reinforcement learning (CDRL) algorithms in which multiple
agents can coordinate over a wireless network is a promising approach to enable
future intelligent and autonomous systems that rely on real-time
decision-making in complex dynamic environments. Nonetheless, in practical
scenarios, CDRL faces many challenges due to the heterogeneity of agents and
their learning tasks, different environments, time constraints of the learning,
and resource limitations of wireless networks. To address these challenges, in
this paper, a novel semantic-aware CDRL method is proposed to enable a group of
heterogeneous untrained agents with semantically-linked DRL tasks to
collaborate efficiently across a resource-constrained wireless cellular
network. To this end, a new heterogeneous federated DRL (HFDRL) algorithm is
proposed to select the best subset of semantically relevant DRL agents for
collaboration. The proposed approach then jointly optimizes the training loss
and wireless bandwidth allocation for the cooperating selected agents in order
to train each agent within the time limit of its real-time task. Simulation
results show the superior performance of the proposed algorithm compared to
state-of-the-art baselines.

提出了一种新的语义感知 CDRL 方法，通过一种新的异构联合 DRL 算法实现了多智能体在无线网络中高效协作，并优化了训练损失和无线带宽分配以满足实时任务的时间限制。与现有算法相比，该方法表现出更优异的性能。