Results 301 to 310 of about 1,052,376 (334)

Coevolutionary Deep Reinforcement Learning

2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for this, selfplay, allows reinforcement learning to develop high performing policies without large datasets or expert knowledge. Despite these benefits, self-play is known to be less sample efficient and suffer unstable learning dynamics.
David Cotton, Jason M. Traish, Zenon Chaczko   +2 more
openaire   +1 more source

Deep Reinforcement Learning: A Survey

IEEE Transactions on Neural Networks and Learning Systems
Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end learning control capabilities. In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and ...
Xu Wang, Xingxing Liang, Dawei Zhao
exaly   +3 more sources

An Overview of Deep Reinforcement Learning

Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering, 2019
As a new machine learning method, deep reinforcement learning has made important progress in various fields of people's production and life since it was proposed. However, there are still many difficulties in function design and other aspects. Therefore, further research on deep reinforcement learning is of great significance for promoting the progress
LiChun Cao, ZhiMin
openaire   +1 more source

DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

Conference on Empirical Methods in Natural Language Processing
Large Language Models (LLMs) equipped with web search capabilities have demonstrated impressive potential for deep research tasks. However, current approaches predominantly rely on either manually engineered prompts (prompt engineering-based) with ...
Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, Pengfei Liu   +6 more
semanticscholar   +1 more source

A Brief Survey of Deep Reinforcement Learning for Intersection Navigation of Autonomous Vehicles

2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
This paper presents a brief survey of deep reinforcement learning (DRL) for intersection navigation in autonomous driving. Intersection navigation poses significant challenges for autonomous driving (AD), considering the dynamic environment, high ...
Yuqi Liu, Qichao Zhang, Dongbin Zhao
semanticscholar   +1 more source

Reinforcement Learning and Deep Reinforcement Learning

2019
In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
F. Richard Yu, Ying He
openaire   +1 more source

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

AAAI Conference on Artificial Intelligence
Reinforcement learning (RL), particularly its combination with deep neural networks, referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated ...
Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Mart'in-Mart'in, Peter Stone   +5 more
semanticscholar   +1 more source

From Reinforcement Learning to Deep Reinforcement Learning: An Overview

2018
This article provides a brief overview of reinforcement learning, from its origins to current research trends, including deep reinforcement learning, with an emphasis on first principles.
Forest Agostinelli, Guillaume Hocquet, Sameer Singh 0001, Pierre Baldi   +3 more
openaire   +1 more source

Deep Reinforcement Learning in Wargaming

Journal of Aerospace Information Systems
We investigate the potential of deep reinforcement learning (RL) for the development of autonomous wargaming agents. We discuss the relevant characteristics of wargaming environments for the design of learning systems, the choice of learning framework, and algorithms. While deep RL has been demonstrated to achieve superhuman levels in various games, we
Giacomo Del Rio, Oleg Szehr, Alessandro Antonucci 0001, Marco Pierbattista, Matthias Sommer, Michael Rüegsegger   +5 more
openaire   +1 more source

Human-level control through deep reinforcement learning

Nature, 2015
Volodymyr Mnih, K. Kavukcuoglu, David Silver, Andrei A. Rusu, J. Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, A. Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, D. Kumaran, Daan Wierstra, S. Legg, D. Hassabis   +18 more
semanticscholar   +1 more source