Game agents are efficiently implemented through different AI techniques, such as neural network, reinforcement learning, and evolutionary search. Although there are many works for each approach, we present a critical analysis and comparison between them, suggesting a common benchmark and parameter configurations. The evolutionary strategy implements the NeuroEvolution of Augmenting Topologies algorithm, while the reinforcement learning agent leverages Q-Learning and Proximal Policy Optimization. We formulate and empirically compare this set of solutions using the Flappy Bird game as a test scenario. We also compare different representations of state and reward functions for each method. All methods were able to generate agents that can play the game, where the NEAT algorithm had the best results, reaching the goal of never losing.