  Reinforcement learning (RL) is a foundation of learning in biological systems
and provides a framework to address numerous challenges with real-world
artificial intelligence applications. Efficient implementations of RL
techniques could allow for agents deployed in edge-use cases to gain novel
abilities, such as improved navigation, understanding complex situations and
critical decision making. Towards this goal, we describe a flexible
architecture to carry out reinforcement learning on neuromorphic platforms.
This architecture was implemented using an Intel neuromorphic processor and
demonstrated solving a variety of tasks using spiking dynamics. Our study
proposes a usable energy efficient solution for real-world RL applications and
demonstrates applicability of the neuromorphic platforms for RL problems.
