Search · four archives

Results for Reinforcement Learning

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

24 papers · ranked by Valyu relevance

01
AarXiv2022
Deep Reinforcement Learning
Aske Plaat
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these elds, computer programs have learned to solve dicult problems. They have learned to y model helicopters and…
93relevance
02
AarXiv2022
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction
Mingqi Yuan
Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which is increasingly involved in smart manufacturing and autonomous driving. However, RL consistently suffers from the explorationexploitation…
89relevance
03
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…
89relevance
04
AarXiv13 Aug 2024
Introduction to Reinforcement Learning
Majid Ghasemi, Dariush Ebrahimi
Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) which focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. An overview of RL is provided in this paper, which discusses its core concepts, methodologies, recent trends, and resources for…
88relevance
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.…
87relevance
06
BbioRxiv24 Feb 2025
Strategic Decision Making in Biological and Artificial Brains
Anushka Deshpande
The aim of this paper is twofold. First, it seeks to uncover the algorithms that humans and other animals employ for learning in decision-making strategies within non-zero-sum games, specifically focusing on fully observable iterated prisoner’s dilemma scenarios. Second, it aims to develop a new model to explain…
86relevance
07
BbioRxiv14 Apr 2022
Reward Bases: Instantaneous reward revaluation with temporal difference learning
Beren Millidge, Mark Walton, Rafal Bogacz
An influential theory posits that dopaminergic neurons in the mid-brain implement a model-free reinforcement learning algorithm based on temporal difference (TD) learning. A fundamental assumption of this model is that the reward function being optimized is fixed. However, for biological creatures the ‘reward function’…
86relevance
08
PPubMed17 Aug 2023
Optimal Reactive Power Dispatch in ADNs using DRL and the Impact of Its Various Settings and Environmental Changes
Tassneem Zamzam, Khaled Shaban, Ahmed Massoud, Luis Hernández-Callejo
'Luis Hernández-Callejo'] Modern active distribution networks (ADNs) witness increasing complexities that require efforts in control practices, including optimal reactive power dispatch (ORPD). Deep reinforcement learning (DRL) is proposed to manage the network’s reactive power by coordinating different resources…
85relevance
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…
85relevance
10
BbioRxiv1 Jun 2024
A melancholy machine: simulated synapse loss induces depression-like behaviors in deep reinforcement learning
Eric Chalmers, Santina Duarte, Xena Al-Hejji, Daniel Devoe + 2 more
Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network -…
85relevance
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…
84relevance
12
AarXiv2 Jul 2025
Quantum reinforcement learning in dynamic environments
Oliver Sefrin, Manuel Radons, Lars Simon, Sabine Wölk
Combining quantum computing techniques in the form of amplitude amplification with classical reinforcement learning has led to the so-called "hybrid agent for quantum-accessible reinforcement learning", which achieves a quadratic speedup in sample complexity for certain learning problems. So far, this hybrid agent has…
84relevance
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…
83relevance
14
AarXiv2022
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey
Arthur Aubret, Laëtitia Matignon, Salima Hassas
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, amongst which we can mention the ability to abstract actions or the…
83relevance
15
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
81relevance
16
AarXiv17 Nov 2025
Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design
Markus D. Solbach, John K. Tsotsos
Reinforcement Learning is a mature technology, often suggested as a potential route towards Artificial General Intelligence, with the ambitious goal of replicating the wide range of abilities found in natural and artificial intelligence, including the complexities of human cognition. While RL had shown successes in…
80relevance
17
BbioRxiv27 Apr 2023
Exploration-exploitation mechanisms in recurrent neural networks and human learners in restless bandit problems
D. Tuzsus, A. Brands, I. Pappas, J. Peters
A key feature of animal and human decision-making is to balance the exploration of unknown options for information gain (directed exploration) versus selecting known options for immediate reward (exploitation), which is often examined using restless bandit tasks. Recurrent neural network models (RNNs) have recently…
80relevance
18
AarXiv2023
An introduction to reinforcement learning for neuroscience
Kristopher T. Jensen
Reinforcement learning has a rich history in neuroscience, from early work on dopamine as a reward prediction error signal for temporal difference learning (Schultz et al., 1997) to recent work suggesting that dopamine could implement a form of 'distributional reinforcement learning' popularized in deep learning…
80relevance
19
BbioRxiv28 Apr 2024
Working memory as a representational template for reinforcement learning
Kengo Shibata, Verena Klar, Sean J Fallon, Masud Husain + 1 more
Working memory (WM) and reinforcement learning (RL) both influence decision-making, but how they interact to affect behaviour remains unclear. We assessed whether RL is influenced by the format of visual stimuli in WM, either feature-based or unified, object-based representations. In a pre-registered paradigm…
80relevance
20
CchemRxiv21 Mar 2023
Reinforcement Learning in Crystal Structure Prediction
Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev + 6 more
Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to…
77relevance
21
CchemRxiv14 Nov 2025
Bringing ML to the real world: rewards are all we need
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
76relevance
22
CchemRxiv8 Aug 2023
Sample Efficient Reinforcement Learning with Active Learning for Molecular Design
Michael Dodds, Jeff Guo, Thomas Löhr, Alessandro Tibo + 2 more
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual…
74relevance
23
CchemRxiv20 Oct 2021
Improving De Novo Molecular Design with Curriculum Learning
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter + 4 more
Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally…
72relevance
24
CchemRxiv4 Apr 2024
Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
70relevance

Results for Reinforcement Learning

Deep Reinforcement Learning

Intrinsically-Motivated Reinforcement Learning: A Brief Introduction

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

Introduction to Reinforcement Learning

Importance of prefrontal meta control in human-like reinforcement learning

Strategic Decision Making in Biological and Artificial Brains

Reward Bases: Instantaneous reward revaluation with temporal difference learning

Optimal Reactive Power Dispatch in ADNs using DRL and the Impact of Its Various Settings and Environmental Changes

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

A melancholy machine: simulated synapse loss induces depression-like behaviors in deep reinforcement learning

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

Quantum reinforcement learning in dynamic environments

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

An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design

Exploration-exploitation mechanisms in recurrent neural networks and human learners in restless bandit problems

An introduction to reinforcement learning for neuroscience

Working memory as a representational template for reinforcement learning

Reinforcement Learning in Crystal Structure Prediction

Bringing ML to the real world: rewards are all we need

Sample Efficient Reinforcement Learning with Active Learning for Molecular Design

Improving De Novo Molecular Design with Curriculum Learning

Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning

Deep Reinforcement Learning

Intrinsically-Motivated Reinforcement Learning: A Brief Introduction

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

Introduction to Reinforcement Learning

Importance of prefrontal meta control in human-like reinforcement learning

Strategic Decision Making in Biological and Artificial Brains

Reward Bases: Instantaneous reward revaluation with temporal difference learning

Optimal Reactive Power Dispatch in ADNs using DRL and the Impact of Its Various Settings and Environmental Changes

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

A melancholy machine: simulated synapse loss induces depression-like behaviors in deep reinforcement learning

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

Quantum reinforcement learning in dynamic environments

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

An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design

Exploration-exploitation mechanisms in recurrent neural networks and human learners in restless bandit problems

An introduction to reinforcement learning for neuroscience

Working memory as a representational template for reinforcement learning

Reinforcement Learning in Crystal Structure Prediction

Bringing ML to the real world: rewards are all we need

Sample Efficient Reinforcement Learning with Active Learning for Molecular Design

Improving De Novo Molecular Design with Curriculum Learning

Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning