Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs. Experimental results on a variety of benchmarks demonstrate the efficacy and scalability of our RL agent, which obtains a significant reduction ($169 \times$) in the number of test patterns required while maintaining or improving coverage $(95.75 \%)$ compared to the state-of-the-art techniques. ACM Reference Format: Vasudev Gohil, Satwik Patnaik, Hao Guo, Dileep Kalathil, and Jeyavijayan (JV) Rajendran. 2022. DETERRENT: Detecting Trojans using Reinforcement Learning. In Proceedings of the 59th ACM Design Automation Conference (DAC ’22), July 10–14, 2022, San Francisco, CA, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3489517.3530518