Reinforcement learning (RL) offers a promising solution when dealing with the general problem of optimal decision and control of agents that interact with uncertain environments. A major challenge of existing algorithms is the slow rate of convergence and long training times especially when dealing with high-dimensional state and action spaces. In our work, we review the use of evolutionary computing as a competitive alternative to training deep neural networks for RL problems. A novel distributed algorithm, efficient model encoding enabling the intuitive application of mutation and crossover is also proposed. Preliminary results on Atari games demonstrate comparable performance to DQN and A3C with a significant speedup.