The ability of neural networks to perform robotic perception and control tasks such as depth and optical flow estimation, simultaneous localization and mapping (SLAM), and automatic control has led to their widespread adoption in recent years. Deep Reinforcement Learning has been used extensively in these settings, as it does not have the unsustainable training costs associated with supervised learning. However, DeepRL suffers from poor sample efficiency, i.e., it requires a large number of environmental interactions to converge to an acceptable solution. Modern RL algorithms such as Deep Q Learning and Soft Actor-Critic attempt to remedy this shortcoming but can not provide the explainability required in applications such as autonomous robotics. Humans intuitively understand the long-time-horizon sequential tasks common in robotics. Properly using such intuition can make RL policies more explainable while enhancing their sample efficiency. In this work, we propose SHIRE, a novel framework for encoding human intuition using Probabilistic Graphical Models (PGMs) and using it in the Deep RL training pipeline to enhance sample efficiency. Our framework achieves 25-78% sample efficiency gains across the environments we evaluate at negligible overhead cost. Additionally, by teaching RL agents the encoded elementary behavior, SHIRE enhances policy explainability. A real-world demonstration further highlights the efficacy of policies trained using our framework.

HONG KONG – Takeda Pharmaceutical Co.'s ambitions to expand in the lucrative U.S. health care market led the drugmaker to begin a painstaking examination of Shire PLC's assets more than two years prior to striking a $62 billion deal. In particular, Shire's neuroscience unit and its gastrointestinal products sparked Takeda's interest. One hurdle, though, gave them pause: the steep purchase price. Then the tide turned in Takeda's favor. Shire's struggling stock performance after its failed sale to AbbVie Inc. and the acquisition spree that followed -- culminating with the $32 billion takeover of Baxalta Inc. -- frustrated investors and prompted concerns about its strategy.