Results 91 to 100 of about 1,056,132 (188)

Deep Reinforcement Learning algorithms learn important classes of repeated games optimally—Theoretical and empirical analysis

Franklin Open
This paper evaluates two prominent Deep Reinforcement Learning algorithms, Deep Q-Learning and Twin Delayed Deep Deterministic Policy Gradient, by comparing their learned policies against analytically derived optimal policies in specific game-theoretic ...
Marvin Bongiovi
doaj   +1 more source

Goal-guided greedy experience replay-enhanced reinforcement learning for efficient autonomous navigation

Scientific Reports
Despite some success in mapless goal-driven navigation using deep reinforcement learning, there is an issue of insufficient experience utilization in deep reinforcement learning-based mapless goal-driven navigation.
Yichun Zeng, Mingshan Xie
doaj   +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

The advancements and applications of deep reinforcement learning in Go [PDF]

ITM Web of Conferences
Combining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI).
Zheng Xutao
doaj   +1 more source

An Invitation to Deep Reinforcement Learning

Foundations and Trends® in Optimization
Training a deep neural network to maximize a target objective has become the standard recipe for successful machine learning over the last decade. These networks can be optimized with supervised learning if the target objective is differentiable. However, this is not the case for many interesting problems. Common objectives like intersection over union
Bernhard Jaeger, Andreas Geiger 0001
openaire   +2 more sources

Improving sample efficiency and exploration in upside-down reinforcement learning

Journal of Information and Intelligence
Supervised learning has been demonstrated to be a stable approach for training deep neural networks. Upside-down reinforcement learning solves reinforcement learning problems by using supervised learning, but this method suffers from weak sample ...
Mohammadreza Nakhaei, Reza Askari Moghadam   +1 more
doaj   +1 more source

Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning

Energy and AI
Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks.
Xuanang Zhang, Xuan Wang, Ping Yuan, Hua Tian, Gequn Shu   +4 more
doaj   +1 more source

Reinforcement learning-based energy management for hybrid electric vehicles: A comprehensive up-to-date review on methods, challenges, and research gaps

Energy and AI
Reinforcement learning is widely used for control applications and has also been successfully implemented for efficient energy management within hybrid electric vehicles.
Mohamed Nadir Boukoberine, Muhammad Fahad Zia, Tarek Berghout, Mohamed Benbouzid   +3 more
doaj   +1 more source

Robust Path Planning via Deep Reinforcement Learning. [PDF]

Sensors (Basel)
Kang D, Park J, Kim P.
europepmc   +1 more source

Morphological symmetry-aware generalized policy network for deep reinforcement learning. [PDF]

Front Robot AI
Hakoda R, Liu Y, Hwang M, Sato Y, Takamatsu J, Ikeuchi K, Oishi T.   +6 more
europepmc   +1 more source