  Hybrid FSO/RF system requires an efficient FSO and RF link switching
mechanism to improve the system capacity by realizing the complementary
benefits of both the links. The dynamics of network conditions, such as fog,
dust, and sand storms compound the link switching problem and control
complexity. To address this problem, we initiate the study of deep
reinforcement learning (DRL) for link switching of hybrid FSO/RF systems.
Specifically, in this work, we focus on actor-critic called Actor/Critic-FSO/RF
and Deep-Q network (DQN) called DQN-FSO/RF for FSO/RF link switching under
atmospheric turbulences. To formulate the problem, we define the state, action,
and reward function of a hybrid FSO/RF system. DQN-FSO/RF frequently updates
the deployed policy that interacts with the environment in a hybrid FSO/RF
system, resulting in high switching costs. To overcome this, we lift this
problem to ensemble consensus-based representation learning for deep
reinforcement called DQNEnsemble-FSO/RF. The proposed novel DQNEnsemble-FSO/RF
DRL approach uses consensus learned features representations based on an
ensemble of asynchronous threads to update the deployed policy. Experimental
results corroborate that the proposed DQNEnsemble-FSO/RF's consensus-learned
features switching achieves better performance than Actor/Critic-FSO/RF,
DQN-FSO/RF, and MyOpic for FSO/RF link switching while keeping the switching
cost significantly low.
