  We propose reinforcement learning on simple networks consisting of random
connections of spiking neurons (both recurrent and feed-forward) that can learn
complex tasks with very little trainable parameters. Such sparse and randomly
interconnected recurrent spiking networks exhibit highly non-linear dynamics
that transform the inputs into rich high-dimensional representations based on
past context. The random input representations can be efficiently interpreted
by an output (or readout) layer with trainable parameters. Systematic
initialization of the random connections and training of the readout layer
using Q-learning algorithm enable such small random spiking networks to learn
optimally and achieve the same learning efficiency as humans on complex
reinforcement learning tasks like Atari games. The spike-based approach using
small random recurrent networks provides a computationally efficient
alternative to state-of-the-art deep reinforcement learning networks with
several layers of trainable parameters. The low-complexity spiking networks can
lead to improved energy efficiency in event-driven neuromorphic hardware for
complex reinforcement learning tasks.
