LTE in unlicensed spectrum (LTE-U) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-U, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-U small base stations (SBSs). The proposed approach enables multiple SBSs to perform dynamic channel selection, carrier aggregation, and fractional spectrum access proactively while guaranteeing fairness with existing WiFi networks and other LTE-U operators. SBSs are modeled as Homo Egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with WLAN and other LTE-U operators over a given time horizon. Simulation results using real data traces show that the proposed scheme can yield up to 28% gains over a conventional reactive approach. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-U network.