Deep reinforcement learning is a powerful technique for creating effective decision-making systems, but its complexity has hindered widespread adoption. Despite the perceived cost of RL, a wide range of interesting applications are already feasible with current techniques. The main barrier to broader use of RL is now the lack of accessible tooling and infrastructure. In this blog post, I introduce the Entity Neural Network (ENN) project, a set of libraries designed to simplify the process of applying RL to complex simulated environments, significantly reducing the required engineering effort, computational cost, and expertise. The enn-trainer RL framework can be seamlessly integrated with complex simulators without requiring users to write custom training code or network architectures. Furthermore, the framework can train RL policies at a rate of more than 100,000 samples/s on a single consumer GPU, achieves similar performance to the IMPALA network architecture while using 50x fewer parameters and 30x-600x less FLOPS, and produces RL agents that are efficient enough to be run in real-time on the CPU inside web browsers.