Hierarchical Reinforcement Learning Explains Task Interleaving Behavior
نویسندگان
چکیده
منابع مشابه
Multiscale Anticipatory Behavior by Hierarchical Reinforcement Learning
In order to establish autonomous behavior for technical systems, the well known trade-off between reactive control and deliberative planning has to be considered. Within this paper, we combine both principles by proposing a two-level hierarchical reinforcement learning scheme to enable the system to autonomously determine suitable solutions to new tasks. The approach is based on a behavior repr...
متن کاملHierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents
This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...
متن کاملAutonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning
Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, whi...
متن کاملComposite Task-Completion Dialogue System via Hierarchical Deep Reinforcement Learning
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical fra...
متن کاملMulti-Task Reinforcement Learning Using Hierarchical Bayesian Models
For this project, the objective was to build a working implementation of a multi-task reinforcement learning (MTRL) agent using a hierarchical Bayesian model (HBM) framework described in the paper “Multitask reinforcement learning: A hierarchical Bayesian approach” (Wilson, et al. 2007). This agent was then to play a modified version of the game of Pacman. In this version of the classic arcade ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Brain & Behavior
سال: 2020
ISSN: 2522-0861,2522-087X
DOI: 10.1007/s42113-020-00093-9