Review Paper
Year: 2022 | Month: April-June | Volume: 7 | Issue: 2 | Pages: 485-491
DOI: https://doi.org/10.52403/ijshr.20220468
Hierarchical Reinforcement Learning: Structuring Decision-Making for Complex AI Tasks
Deekshitha Kosaraju
Independent Researcher, Texas, USA
ABSTRACT
Hierarchical Reinforcement Learning (abbreviated as HRL) represents a progress in the realm of artificial intelligence (AI) focusing on handling the increasing complexity of decision-making processes effectively. By breaking down tasks into smaller sub tasks that are easier to manage and comprehend; HRL plays a key role in enhancing the learning efficiency of AI models. The hierarchical system mirrors decision making approaches by breaking down complex goals into smaller achievable steps; this allows reinforcement learning agents to effectively learn and adjust to dynamic environments, with limited rewards and long-term objectives. In contrast to the limitations faced by flat reinforcement learning techniques in terms of scalability and efficiency Hierarchical Reinforcement Learning (HRL) introduces a higher-level policy that controls the series and implementation of sub tasks. This study delves into the evolution, application, and influence of reinforcement learning models in artificial intelligence. We explore HRL frameworks, such, as the Options framework and temporal adaptive models that offer a systematic decision-making method. Furthermore, the study analyzes the role HRL plays in fields including robotics, self-governing systems, and game artificial intelligence. HRL is on the brink of transforming how AI tackles challenges by shaping decision making processes in intricate settings that mimic real world scenarios. This piece explores the issue at hand along, with its solutions and applications while discussing the influence and extent of HRL in enhancing AI technologies.
Keywords: Hierarchical Reinforcement Learning, AI, Reinforcement Learning, Decision-Making, Sub-tasks, Options Framework, Temporal-Adaptive Models, Robotics, Autonomous Systems, Game AI, Artificial Intelligence.