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Hierarchical reinforcement learning based on macro actions
Complex & Intelligent SystemsThe large action space is a key challenge in reinforcement learning. Although hierarchical methods have been proven to be effective in addressing this issue, they are not fully explored.
Hao Jiang +5 more
doaj +1 more source
A Neural Signature of Hierarchical Reinforcement Learning [PDF]
Neuron, 2011 Human behavior displays hierarchical structure: simple actions cohere into subtask sequences, which work together to accomplish overall task goals. Although the neural substrates of such hierarchy have been the target of increasing research, they remain poorly understood.
Ribas-Fernandes, José J.F. +6 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Cuttlebone‐inspired metamaterials exploit a septum‐wall architecture to achieve excellent mechanical and functional properties. This review classifies existing designs into direct biomimetic, honeycomb‐type, and strut‐type architectures, summarizes governing design principles, and presents a decoupled design framework for interpreting multiphysical ...
Xinwei Li, Zhendong Li
wiley +1 more source
Network-Wide Traffic Signal Control Based on MARL With Hierarchical Nash-Stackelberg Game Model
IEEE Access, 2023 Network-wide traffic signal control is an important means of relieving urban congestion, reducing traffic accidents, and improving traffic efficiency.
Hui Shen +5 more
doaj +1 more source
Hierarchical Reinforcement Learning - A Survey [PDF]
International Journal of Computing and Digital Systems, 2015 Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be more powerful than solving the entire problem will be our concern in this paper.
openaire +1 more source
Advanced Functional Materials, EarlyView.This review maps how MOFs can manage hazardous gases by combining adsorption, neutralization, and reutilization, enabling sustainable air‐pollution control. Covering chemical warfare agent simulants, SO2, NOx, NH3, H2S, and volatile organic compounds, it highlights structure‐guided strategies that boost selectivity, water tolerance, and cycling ...
Yuanmeng Tian +8 more
wiley +1 more source
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
, 2018 Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we
Gao, Jianfeng +4 more
core +1 more source
Advanced Functional Materials, EarlyView.We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu +3 more
wiley +1 more source
SHIRO: Soft Hierarchical Reinforcement Learning
, 2022 Hierarchical Reinforcement Learning (HRL) algorithms have been demonstrated to perform well on high-dimensional decision making and robotic control tasks. However, because they solely optimize for rewards, the agent tends to search the same space redundantly. This problem reduces the speed of learning and achieved reward.
Watanabe, Kandai +2 more
openaire +2 more sources
Benchmarking Deep Reinforcement Learning for Continuous Control [PDF]
, 2016 Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Abbeel, Pieter +4 more
core +2 more sources