# Hierarchical Reinforcement Learning

97 papers with code • 1 benchmarks • 2 datasets

## Libraries

Use these libraries to find Hierarchical Reinforcement Learning models and implementations## Most implemented papers

# A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions.

# Data-Efficient Hierarchical Reinforcement Learning

In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.

# Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.

# Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges wih probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction.

# Learning Multi-Level Hierarchies with Hindsight

Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions.

# Hierarchical Reinforcement Learning for Automatic Disease Diagnosis

Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making.

# Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning

Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.

# Stochastic Neural Networks for Hierarchical Reinforcement Learning

Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks.

# A Hierarchical Framework for Relation Extraction with Reinforcement Learning

The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.

# Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies.