  This paper utilizes Reinforcement Learning (RL) as a means to automate the
Hardware Trojan (HT) insertion process to eliminate the inherent human biases
that limit the development of robust HT detection methods. An RL agent explores
the design space and finds circuit locations that are best for keeping inserted
HTs hidden. To achieve this, a digital circuit is converted to an environment
in which an RL agent inserts HTs such that the cumulative reward is maximized.
Our toolset can insert combinational HTs into the ISCAS-85 benchmark suite with
variations in HT size and triggering conditions. Experimental results show that
the toolset achieves high input coverage rates (100\% in two benchmark
circuits) that confirms its effectiveness. Also, the inserted HTs have shown a
minimal footprint and rare activation probability.
