Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) is a subfield of Reinforcement Learning (RL) that introduces a hierarchical structure to the decision-making process. This approach aims to simplify complex tasks by breaking them down into manageable subtasks, thereby improving the efficiency and scalability of RL algorithms.


Hierarchical Reinforcement Learning is a method that structures the learning process into multiple levels of abstraction. It decomposes a complex task into a hierarchy of simpler subtasks, each of which can be solved independently. This hierarchical structure allows the agent to learn and make decisions at different levels of granularity, facilitating faster learning and better generalization across tasks.

Why it Matters

In traditional RL, an agent learns to map states to actions to maximize a reward signal. However, as the complexity of the task increases, the state-action space can become prohibitively large, making learning inefficient or even infeasible. HRL addresses this issue by decomposing the task into a hierarchy of subtasks, each with its own state-action space. This reduces the complexity of the learning problem, accelerates learning, and improves the agent’s ability to generalize from one task to another.

How it Works

In HRL, a task is divided into a hierarchy of subtasks, each represented by a Semi-Markov Decision Process (SMDP). At the top of the hierarchy is the root task, which encompasses the entire problem. Each subtask in the hierarchy has its own policy, which maps states to actions or lower-level subtasks.

The learning process in HRL involves two main steps: intra-option learning and inter-option learning. In intra-option learning, the agent learns the policy for each subtask independently. In inter-option learning, the agent learns how to sequence the subtasks to solve the root task.

Use Cases

HRL has been successfully applied in various domains, including robotics, game playing, and autonomous driving. In robotics, HRL can simplify complex tasks such as object manipulation or navigation by breaking them down into simpler subtasks like reaching, grasping, or avoiding obstacles. In game playing, HRL can help an agent learn to play complex games by decomposing them into simpler subgames. In autonomous driving, HRL can be used to decompose the driving task into subtasks like lane following, overtaking, and turning.

Key Challenges

While HRL offers many advantages, it also presents several challenges. One key challenge is defining the right hierarchy of subtasks. If the hierarchy is too shallow or too deep, it can negatively impact learning efficiency. Another challenge is transferring knowledge across tasks. While HRL is designed to facilitate transfer learning, achieving this in practice can be difficult due to differences in the state-action spaces of different tasks.

Further Reading

  1. Sutton, R. S., Precup, D., & Singh, S. (1999). Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial intelligence, 112(1-2), 181-211.
  2. Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227-303.
  3. Vezhnevets, A. S., Osindero, S., Schaul, T., Heess, N., Jaderberg, M., Silver, D., & Kavukcuoglu, K. (2017). Feudal networks for hierarchical reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 3540-3549). JMLR. org.