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Adaptive Task-Oriented Locomotion Control of a 2D Planar Robotic Fish Model Using Deep Reinforcement Learning and Sensory-Feedback CPG Network. [PDF]

Biomimetics (Basel)
Ozmen Koca G, Korkmaz D, Bal C, Ay M, Akpolat ZH.   +4 more
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Deep learning and Reinforcement Learning.

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A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

Intelligent Service Robotics, 2021
This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a ...
Eduardo F. Morales 0001, Rafael Murrieta-Cid, Israel Becerra, Marco A. Esquivel-Basaldua   +3 more
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Learning to Drive with Deep Reinforcement Learning

2021 13th International Conference on Knowledge and Smart Technology (KST), 2021
Autonomous driving cars are important due to improved safety and fuel efficiency. Various techniques have been described to consider only a single task, for example, recognition, prediction, and planning with supervised learning techniques. Some limitations of previous studies are: (1) human bias from human demonstration; (2) the need for multiple ...
Nut Chukamphaeng, Kitsuchart Pasupa, Martin Antenreiter, Peter Auer   +3 more
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Deep reinforcement learning: a survey

Frontiers of Information Technology & Electronic Engineering, 2020
Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems.
Haonan Wang, Ning Liu 0015, Yiyun Zhang, Dawei Feng, Feng Huang, Dong Sheng Li 0001, Yiming Zhang 0003   +6 more
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Exploration in deep reinforcement learning: A survey

Information Fusion, 2022
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly.
Minwoo Kim, Hyondong Oh, Pawel Ladosz
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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
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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
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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
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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
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