相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Safe deep reinforcement learning-based adaptive control for USV interception mission
Bin Du et al.
OCEAN ENGINEERING (2022)
AUV path tracking with real-time obstacle avoidance via reinforcement learning under adaptive constraints
Chenming Zhang et al.
OCEAN ENGINEERING (2022)
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
Bryon Tjanaka et al.
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22) (2022)
The object-oriented dynamic task assignment for unmanned surface vessels
Bin Du et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)
Stochastic Genetic Algorithm-Assisted Fuzzy Q-Learning for Robotic Manipulators
Amit Kukker et al.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2021)
A novel real-time design for fighting game AI
Gia Thuan Lam et al.
EVOLVING SYSTEMS (2021)
Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis
Yue Wen et al.
IEEE TRANSACTIONS ON CYBERNETICS (2020)
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications
Thanh Thi Nguyen et al.
IEEE TRANSACTIONS ON CYBERNETICS (2020)
Mastering Atari, Go, chess and shogi by planning with a learned model
Julian Schrittwieser et al.
NATURE (2020)
Genetic Algorithm-Optimized Fuzzy Lyapunov Reinforcement Learning for Nonlinear Systems
Amit Kukker et al.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2020)
Hierarchical Reinforcement Learning With Monte Carlo Tree Search in Computer Fighting Game
Ivan Pereira Pinto et al.
IEEE TRANSACTIONS ON GAMES (2019)
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals et al.
NATURE (2019)
A new multi-satellite autonomous mission allocation and planning method
Bin Du et al.
ACTA ASTRONAUTICA (2019)
Integrating Agent Actions with Genetic Action Sequence Method
Man-Je Kim et al.
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION) (2019)
Mastering the game of Go with deep neural networks and tree search
David Silver et al.
NATURE (2016)
Model-Free Optimal Tracking Control via Critic-Only Q-Learning
Biao Luo et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2016)
Applying and Improving Monte-Carlo Tree Search in a Fighting Game AI
Makoto Ishihara et al.
13TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER ENTERTAINMENT TECHNOLOGY (ACE 2016) (2016)
Human-level control through deep reinforcement learning
Volodymyr Mnih et al.
NATURE (2015)
A Survey of Monte Carlo Tree Search Methods
Cameron B. Browne et al.
IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES (2012)
Study of genetic algorithm with reinforcement learning to solve the TSP
Fei Liu et al.
EXPERT SYSTEMS WITH APPLICATIONS (2009)
Bandit based Monte-Carlo planning
Levente Kocsis et al.
MACHINE LEARNING: ECML 2006, PROCEEDINGS (2006)