Hierarchical Reinforcement Learning
88 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Hierarchical Reinforcement Learning
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Libraries
Use these libraries to find Hierarchical Reinforcement Learning models and implementations
Gibson Environment
bipedal-skills
Most implemented papers
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
In this paper we propose Reward Machines {—} a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition.
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies.
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency.
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
LittleYUYU/Interactive-Semantic-Parsing
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Given a text description, most existing semantic parsers synthesize a program in one shot.
Combining imagination and heuristics to learn strategies that generalize
CoAxLab/azad
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Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot adapt.
Diversity-Driven Extensible Hierarchical Reinforcement Learning
YuhangSong/DEHRL
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However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive.
Learning Actionable Representations with Goal-Conditioned Policies
elitalobo/Hierarchical-RL-Algorithms
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Most prior work on representation learning has focused on generative approaches, learning representations that capture all underlying factors of variation in the observation space in a more disentangled or well-ordered manner.
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
TakaOsa/adInfoHRL
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ICLR 2019
However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task.
Certified Reinforcement Learning with Logic Guidance
Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems.
Model Primitive Hierarchical Lifelong Reinforcement Learning
sisl/MPHRL
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Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult.
