Hierarchical Reinforcement Learning | by Ankita Sinha | Towards Data Science

Hierarchical Reinforcement Learning decomposes long horizon decision making process into simpler sub-tasks. This idea is very similar to breaking down large number of lines of code to smaller functions each performing a very specific task. Let’s look at an example, Suppose the agent has to clear or set a dining table. This includes the task of reaching and grasping dishes. These are high level tasks. On a lower level, it requires the task of controlling and moving the limbs and then the fingers to reach out and grasp objects and subsequently put them in the proper place. Hierarchical Reinforcement Learning is designed with the same logic. There are multiple levels of policies with each policy handling a lower level task like moving the fingers and the higher level policies handling tasks like grasping the objects. HRL gives us multiple benefits during training and exploration: A few common architectures for HRL are- Lets look at how to build your own Option-Critic framework in a simple

Hierarchical Reinforcement Learning | Towards Data Science Skip to content Machine Learning Hierarchical Reinforcement Learning With Options-Critic framework using tabular Q-Learning Ankita Sinha Feb 11, 2022 4 min read Share unsplash.com Hierarchical Reinforcement Learning decomposes long horizon decision making process into simpler sub-tasks. This idea is very similar to breaking down large number of lines of code to smaller functions each performing a very specific task. Let’s look at an example, Suppose the agent has to clear or set a dining table. This includes the task of reaching

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