  Recently, significant progress has been made in solving sophisticated
problems among various domains by using reinforcement learning (RL), which
allows machines or agents to learn from interactions with environments rather
than explicit supervision. As the end of Moore's law seems to be imminent,
emerging technologies that enable high performance neuromorphic hardware
systems are attracting increasing attention. Namely, neuromorphic architectures
that leverage memristors, the programmable and nonvolatile two-terminal
devices, as synaptic weights in hardware neural networks, are candidates of
choice to realize such highly energy-efficient and complex nervous systems.
However, one of the challenges for memristive hardware with integrated learning
capabilities is prohibitively large number of write cycles that might be
required during learning process, and this situation is even exacerbated under
RL situations. In this work we propose a memristive neuromorphic hardware
implementation for the actor-critic algorithm in RL. By introducing a two-fold
training procedure (i.e., ex-situ pre-training and in-situ re-training) and
several training techniques, the number of weight updates can be significantly
reduced and thus it will be suitable for efficient in-situ learning
implementations. As a case study, we consider the task of balancing an inverted
pendulum, a classical problem in both RL and control theory. We believe that
this study shows the promise of using memristor-based hardware neural networks
for handling complex tasks through in-situ reinforcement learning.
