  Reinforcement learning augmented by the representational power of deep neural
networks, has shown promising results on high-dimensional problems, such as
game playing and robotic control. However, the sequential nature of these
problems poses a fundamental challenge for computational efficiency. Recently,
alternative approaches such as evolutionary strategies and deep neuroevolution
demonstrated competitive results with faster training time on distributed CPU
cores. Here, we report record training times (running at about 1 million frames
per second) for Atari 2600 games using deep neuroevolution implemented on
distributed FPGAs. Combined hardware implementation of the game console, image
pre-processing and the neural network in an optimized pipeline, multiplied with
the system level parallelism enabled the acceleration. These results are the
first application demonstration on the IBM Neural Computer, which is a custom
designed system that consists of 432 Xilinx FPGAs interconnected in a 3D mesh
network topology. In addition to high performance, experiments also showed
improvement in accuracy for all games compared to the CPU-implementation of the
same algorithm.
