Results 121 to 130 of about 198,528 (317)

Learning Representations in Reinforcement Learning

, 2019
Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection policy to increase rewarding experiences in their environments. Temporal Difference (TD) learning algorithm, a model-free RL method, attempts to find an optimal policy through learning the values of agent's actions at any state by computing the expected ...
openaire   +2 more sources

Refinement of biologically inspired models of reinforcement learning

, 2010
Reinforcement learning occurs when organisms adapt the propensities of given behaviours on the basis of associations with reward and punishment. Currently, reinforcement learning models have been validated in minimalist environments in which only 1-2 ...
Aquili, Luca
core  

Understanding representation learning for deep reinforcement learning

Representation learning is essential to practical success of reinforcement learning. Through a state representation, an agent can describe its environment to efficiently explore the state space, generalize to new states and perform credit assignment from
Le Lan, Charline
core   +1 more source

Texoskeletons: Developing the Fundamental Technologies for Creating Intelligent Soft Robotic Clothing With Integrated 1D Sensors and Actuators

Advanced Functional Materials, EarlyView.
ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak, Rameesh Bulathsinghala, Jack Varga, Shuxin Meng, Kalana Marasinghe, Imaad Refai, Biyon Fernando, Amitkumar Patel, Chamika M. Halloluwa‐Arachchige, Carlos Oliveira, Arash M. Shahidi, Zahra Rahemtulla, Alexander Turner, Ziyun Ding, Bernard X. W. Liew, Andy Kerr, Ezio Preatoni, Theo Hughes‐Riley, Ishara Dharmasena, Pasindu Lugoda   +19 more
wiley   +1 more source

A Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes

Sir Syed University Research Journal of Engineering and Technology
Reinforcement learning (RL) has emerged as a powerful tool for creating artificial intelligence systems (AIS) and solving problems which require sequential decision-making. Reinforcement learning has achieved some impressive achievements in recent years,
Muhammad Azhar, Mansoor Ahmed Khuhro, Muhammad Waqas, Umair Saeed, Mehar Khan Niazi   +4 more
doaj   +1 more source

Learning to deal with risk: what does reinforcement learning tell us about risk atittudes?. [PDF]

People are generally reluctant to accept risk. In particular, people overvaluate outcomes that are considered certain, relative to outcomes which are merely probable.
Burgos, Albert
core  

Deep Reinforcement Learning Approaches for the Game of Briscola [PDF]

, 2023
openReinforcement learning is increasingly becoming one of the most interesting areas of research in recent years. It is a machine learning approach that aims to design autonomous agents capable of learning from interaction with the envi- ronment ...
SINGH, AMANPREET
core  

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Clustered Reinforcement Learning

CoRR, 2019
16pages, 3 ...
Xiao Ma, Shen-Yi Zhao, Wu-Jun Li
openaire   +2 more sources

Designed Lewis Acid–Base Passivation for High Performance Perovskite Solar Cells

Advanced Functional Materials, EarlyView.
ABSTRACT Silicon's high cost and long energy payback time remain major barriers to the global expansion of solar power. In contrast, metal–halide perovskites offer abundant, solution‐processable absorbers, and have achieved efficiencies of 25%–30%, positioning them as strong competitors to silicon.
Afna Manaf, Hiba Shahulhameed, Bhabani Sankar Swain, Shengzhong (Frank) Liu, Adel Najar   +4 more
wiley   +1 more source