  Re-configurable robots have more utility and flexibility for many real-world
tasks. Designing a learning agent to operate such robots requires adapting to
different configurations. Here, we focus on robotic arms with multiple rigid
links connected by joints. We propose a deep reinforcement learning agent with
sequence neural networks embedded in the agent to adapt to robotic arms that
have a varying number of links. Further, with the additional tool of domain
randomization, this agent adapts to different configurations. We perform
simulations on a 2D N-link arm to show the ability of our network to transfer
and generalize efficiently.
