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18 papers · ranked by Valyu relevance

01
PPubMed11 Apr 2024
Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review
Mokhaled N. A. Al-Hamadani, Mohammed A. Fadhel, Laith Alzubaidi, Harangi Balazs + 2 more
'Harangi Balazs' 'Antonio Fernández-Caballero' 'Dominique Gruyer'] Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential…
94relevance
02
PPubMed5 Apr 2023
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
Dong Han, Beni Mulyana, Vladimir Stankovic, Samuel Cheng + 1 more
'Alberto Borboni'] Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin…
93relevance
03
PPubMed11 Feb 2021
Reinforcement Learning Approaches in Social Robotics
Neziha Akalin, Amy Loutfi, Anne Schmitz, Cosimo Distante
This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and…
92relevance
04
PPubMed11 May 2021
Learning Macromanagement in Starcraft by Deep Reinforcement Learning
Wenzhen Huang, Qiyue Yin, Junge Zhang, Kaiqi Huang + 2 more
'Maja Pušnik'] StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the…
92relevance
05
PPubMed21 Dec 2022
Importance of prefrontal meta control in human-like reinforcement learning
Jee Hang Lee, Joel Z. Leibo, Su Jin An, Sang Wan Lee
Recent investigation on reinforcement learning (RL) has demonstrated considerable flexibility in dealing with various problems. However, such models often experience difficulty learning seemingly easy tasks for humans. To reconcile the discrepancy, our paper is focused on the computational benefits of the brain's RL.…
91relevance
06
PPubMed28 Jan 2014
A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning
Yuchen Fu, Quan Liu, Xionghong Ling, Zhiming Cui
Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of “curse of dimensionality,” which means that the states space will grow…
90relevance
07
PPubMed5 Apr 2017
Multiagent cooperation and competition with deep reinforcement learning Multiagent deep reinforcement learning
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin + 5 more
'Kristjan Korjus' 'Juhan Aru' 'Jaan Aru' 'Raul Vicente' 'Cheng-Yi Xia'] Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by…
90relevance
08
PPubMed28 Oct 2016
‘Proactive’ use of cue-context congruence for building reinforcement learning’s reward function
Judit Zsuga, Klara Biro, Gabor Tajti, Magdolna Emma Szilasi + 3 more
'Csaba Papp' 'Bela Juhasz' 'Rudolf Gesztelyi'] Background Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is…
90relevance
09
PPubMed25 Sep 2022
Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty
MyeongSeop Kim, Jung-Su Kim, Myoung-Su Choi, Jae-Han Park + 2 more
'Gianni D’Angelo' 'Arcangelo Castiglione'] Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the…
89relevance
10
PPubMed4 Dec 2024
Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning
Yana Yang, Meng Xi, Huiao Dai, Jiabao Wen + 2 more
'Sergio Toral Marín'] Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment. It is an important approach to solving sequential decision-making problems. By combining it with deep…
89relevance
11
PPubMed7 Jul 2023
A Multiphase Semistatic Training Method for Swarm Confrontation Using Multiagent Deep Reinforcement Learning
He Cai, Yaoguo Luo, Huanli Gao, Jiale Chi + 1 more
In this paper, we propose a multiphase semistatic training method for swarm confrontation using multi-agent deep reinforcement learning. In particular, we build a swarm confrontation game, the 3V3 tank fight, based on the Unity platform and train the agents by a MDRL algorithm called MA-POCA, coming with the ML-Agent…
88relevance
12
PPubMed14 Feb 2019
Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations
Günther Palm, Friedhelm Schwenker
Research on artificial development, reinforcement learning, and intrinsic motivations like curiosity could profit from the recently developed framework of multi-objective reinforcement learning. The combination of these ideas may lead to more realistic artificial models for life-long learning and goal directed behavior…
87relevance
13
PPubMed21 Nov 2022
Deep reinforcement learning for optimal experimental design in biology Deep reinforcement learning for optimal experimental design in biology
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes + 1 more
'Mark Alber'] The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in…
87relevance
14
PPubMed25 Aug 2025
Weber–Fechner law in temporal difference learning derived from control as inference Takahashi et al. 10.3389/frobt.2025.1649154
Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara
This study investigates a novel nonlinear update rule for value and policy functions based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without any bias. On the…
86relevance
15
PPubMed26 Aug 2022
Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review
Benton Girdler, William Caldbeck, Jihye Bae
Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but…
85relevance
16
PPubMed26 Sep 2022
A Needs Learning Algorithm Applied to Stable Gait Generation of Quadruped Robot
Hanzhong Zhang, Jibin Yin, Haoyang Wang, Michael E. Hahn
Based on Maslow’s hierarchy of needs theory, we have proposed a novel machine learning algorithm that combines factors of the environment and its own needs to make decisions for different states of an agent. This means it can be applied to the gait generation of a quadruped robot, which needs to make demand decisions.…
83relevance
17
PPubMed21 Apr 2026
A brief history of dopamine prediction errors
Biru B. Dudhabhate, Kauê M. Costa
Dopamine signaling has become closely associated with reward prediction errors (RPEs)-the difference between expected and experienced value. Although not without controversy, the dopamine RPE hypothesis is one of the most influential ideas in neuroscience. This review briefly summarizes its origins, empirical…
82relevance
18
PPubMed10 Feb 2023
An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey
Arthur Aubret, Laetitia Matignon, Salima Hassas, Daniel Polani + 1 more
'Domenico Maisto'] The reinforcement learning (RL) research area is very active, with an important number of new contributions, especially considering the emergent field of deep RL (DRL). However, a number of scientific and technical challenges still need to be resolved, among which we acknowledge the ability to…
82relevance

Results for Reinforcement Learning

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Reinforcement Learning Approaches in Social Robotics

Learning Macromanagement in Starcraft by Deep Reinforcement Learning

Importance of prefrontal meta control in human-like reinforcement learning

A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning

Multiagent cooperation and competition with deep reinforcement learning Multiagent deep reinforcement learning

‘Proactive’ use of cue-context congruence for building reinforcement learning’s reward function

Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty

Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning

A Multiphase Semistatic Training Method for Swarm Confrontation Using Multiagent Deep Reinforcement Learning

Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations

Deep reinforcement learning for optimal experimental design in biology Deep reinforcement learning for optimal experimental design in biology

Weber–Fechner law in temporal difference learning derived from control as inference Takahashi et al. 10.3389/frobt.2025.1649154

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

A Needs Learning Algorithm Applied to Stable Gait Generation of Quadruped Robot

A brief history of dopamine prediction errors

An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Reinforcement Learning Approaches in Social Robotics

Learning Macromanagement in Starcraft by Deep Reinforcement Learning

Importance of prefrontal meta control in human-like reinforcement learning

A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning

Multiagent cooperation and competition with deep reinforcement learning Multiagent deep reinforcement learning

‘Proactive’ use of cue-context congruence for building reinforcement learning’s reward function

Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty

Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning

A Multiphase Semistatic Training Method for Swarm Confrontation Using Multiagent Deep Reinforcement Learning

Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations

Deep reinforcement learning for optimal experimental design in biology Deep reinforcement learning for optimal experimental design in biology

Weber–Fechner law in temporal difference learning derived from control as inference Takahashi et al. 10.3389/frobt.2025.1649154

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

A Needs Learning Algorithm Applied to Stable Gait Generation of Quadruped Robot

A brief history of dopamine prediction errors

An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey