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With the rapid adoption of fifth-generation (5G) communication systems and the increasing data demand per device, communication traffic is soaring to reach unprecedented numbers in the upcoming years. Moreover, the traffic is often unevenly distributed across frequency bands and base stations, thereby resulting in a degradation of the network throughput and user experience. Thus, load balancing has become a key technique to adjust the traffic load by offloading users from overloaded cells to less crowded neighboring ones. In this thesis, we study multi-objective reinforcement learning (MORL) and meta reinforcement learning (meta-RL) for load balancing to learn highly customized policies for different trade-offs between network performance metrics. We begin with a thorough review of existing load balancing literature to motivate the need for better algorithms that can further improve the network performance and the user's quality of service (QoS). Specifically, we emphasize the importance of a multi-objective approach to solve the load balancing problem since network providers aim to simultaneously optimize multiple conflicting objectives by adjusting the load balancing parameters. Using MORL, we formulate communication load balancing as a multi-objective control problem where the agent seeks to find optimal policies depending on possible trade-offs between the network performance indicators. Motivated by the dynamic nature of wireless networks, we propose a practical algorithm based on meta-RL concepts to compute a general load balancing policy capable of rapidly adjusting to new trade-offs. Indeed, the learned meta-policy …