Multi-agent reinforcement learning: A review of challenges and applications
In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
Beyond the feedforward sweep: feedback computations in the visual cortex
Visual perception involves the rapid formation of a coarse image representation at the onset
of visual processing, which is iteratively refined by late computational processes. These …
of visual processing, which is iteratively refined by late computational processes. These …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model
Recently, intelligent video surveillance applications have become essential in public
security by the use of computer vision technologies to investigate and understand long video …
security by the use of computer vision technologies to investigate and understand long video …
Collaborative learning for faster stylegan embedding
The latent code of the recent popular model StyleGAN has learned disentangled
representations thanks to the multi-layer style-based generator. Embedding a given image …
representations thanks to the multi-layer style-based generator. Embedding a given image …
Weighing counts: Sequential crowd counting by reinforcement learning
We formulate counting as a sequential decision problem and present a novel crowd
counting model solvable by deep reinforcement learning. In contrast to existing counting …
counting model solvable by deep reinforcement learning. In contrast to existing counting …
Multi-level policy and reward-based deep reinforcement learning framework for image captioning
Image captioning is one of the most challenging tasks in AI because it requires an
understanding of both complex visuals and natural language. Because image captioning is …
understanding of both complex visuals and natural language. Because image captioning is …
PixelRL: Fully convolutional network with reinforcement learning for image processing
This article tackles a new problem setting: reinforcement learning with pixel-wise rewards
(pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has …
(pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has …
Collaborative deep reinforcement learning for multi-object tracking
In this paper, we propose a collaborative deep reinforcement learning (C-DRL) method for
multi-object tracking. Most existing multi-object tracking methods employ the tracking-by …
multi-object tracking. Most existing multi-object tracking methods employ the tracking-by …