<ul><li>A novel multi-layered hierarchical HITL DRL algorithm is introduced for decision-making problems.</li><li>The algorithm combines self learning, imitation learning, and transfer learning.</li><li>Human inputs in the form of reward, action, and demonstration are considered.</li><li>Multiple challenges, trade-offs, and advantages of HITL are discussed in solving complex problems.</li></ul>

A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs

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