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Incremental Multi-Step Q-Learning
[chapter]
1994
Machine Learning Proceedings 1994
Preserved Fulltext
This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamicprogramming based reinforcement learning method, with the TD(A) return estimation process, which is typically ...
The resulting algorithm, Q(A)-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. ...
Introduction The incremental multi-step Q-learning (Q(A)-learning) method is a new direct (or modelfree) algorithm that extends the one-step Q-learning algorithm (Watkins, 1989 ) by combining it with ...
doi:10.1016/b978-1-55860-335-6.50035-0
dblp:conf/icml/PengW94
fatcat:vgztqp6jxbd3habgyvwaku56uu
Incremental multi-step Q-learning
1996
Machine Learning
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This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamicprogramming based reinforcement learning method, with the TD(A) return estimation process, which is typically ...
The resulting algorithm, Q(A)-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. ...
Introduction The incremental multi-step Q-learning (Q(A)-learning) method is a new direct (or modelfree) algorithm that extends the one-step Q-learning algorithm (Watkins, 1989 ) by combining it with ...
doi:10.1007/bf00114731
fatcat:uy3yg4t3cbeirjjvyqx35g5yiy
Using multi-agent systems for learning optimal policies for complex problems
2007
Proceedings of the 45th annual southeast regional conference on - ACM-SE 45
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The basic idea is to apply reinforcement learning and to incrementally acquire knowledge about the implicit parameters dependencies. Based on the obtained data an optimal strategy is learned. ...
For speeding up the learning process a multi-agent architecture is applied, that supports the simultaneous analysis of alternative strategies. ...
Moreover Q-Learning works incrementally and improves the discovered solutions over time. ...
doi:10.1145/1233341.1233385
dblp:conf/ACMse/LommatzschA07
fatcat:ql4wbfmn6zamhlg2ii35gk7k74
Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos
[chapter]
2011
Lecture Notes in Computer Science
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In this paper, we propose a new incremental active learning algorithm based on multiple SVM for image and video annotation. ...
Active learning with multiple classifiers has shown good performance for concept indexing in images or video shots in the case of highly imbalanced data. ...
Fig. 1 . 1 The framework of the proposed incremental method with 40 steps. ...
doi:10.1007/978-3-642-17832-0_23
fatcat:ioap2si34zchbm5eoilt226xki
Incremental reinforcement learning for designing multi-agent systems
2001
Proceedings of the fifth international conference on Autonomous agents - AGENTS '01
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One solution to automatically build such large Multi-Agent Systems is to use decentralized learning : each agent learns by itself its own behavior. ...
Our results show that incremental learning leads to better performances than more classical techniques. We then discuss several improvements which could lead to even better performances. ...
Another aspect of using Q-Learning in a multi-agent system like we did still puzzles us. ...
doi:10.1145/375735.375826
dblp:conf/agents/BuffetDC01
fatcat:qsczgp7lejhmfitkmpelopgz7q
Fast Transformer Decoding: One Write-Head is All You Need
[article]
2019
arXiv
pre-print
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requirements of incremental decoding. ...
Multi-head attention layers, as used in the Transformer neural sequence model, are a powerful alternative to RNNs for moving information across and between sequences. ...
part of the model took 222ms and each incremental step of the decoder took 47ms. ...
arXiv:1911.02150v1
fatcat:qwgqn3mcvnfv3b4kwemuoh4v4a
Knowledge transfer in multi-agent reinforcement learning with incremental number of agents
2022
Journal of Systems Engineering and Electronics
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In this paper, the reinforcement learning method for cooperative multi-agent systems (MAS) with incremental number of agents is studied. ...
The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. ...
reinforcement learning with incremental number of agents ...
doi:10.23919/jsee.2022.000045
fatcat:tw3nziv7ezdhpaveqyrih57oke
Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model
2018
International Journal of Advanced Computer Science and Applications
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An incremental method is also presented to create and tune our fuzzy model that reduces the high computational complexity of the multi-agent systems. ...
its sequence of action-observation from previous communication, up to the current time step. ...
Incremental Learning An incremental learning is a method that creates a model by recursively extracting required information from sequence of incoming data. ...
doi:10.14569/ijacsa.2018.090822
fatcat:rwld75dp7jhd5bnv5nlj7tdrae
Mnemonics Training: Multi-Class Incremental Learning without Forgetting
[article]
2020
arXiv
pre-print
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Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. ...
However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. ...
Our work is conducted on the benchmarks of the latter one called multi-class incremental learning (MCIL). ...
arXiv:2002.10211v4
fatcat:zz4vaugqxreo5hl7phs46qhu5i
Incremental Sequence Learning
[article]
2016
arXiv
pre-print
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Incremental Sequence Learning starts out by using only the first few steps of each sequence as training data. ...
We study incremental learning in the context of sequence learning, using generative RNNs in the form of multi-layer recurrent Mixture Density Networks. ...
Sequence Learning We study incremental learning in the context of sequence learning. The aim in sequence learning is to predict, given a step of the sequence, what the next step will be. ...
arXiv:1611.03068v2
fatcat:bai3txvuavdsllqwdi2rsz6mlu
Stochastic Gradient Trees
[article]
2019
arXiv
pre-print
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with batch multi-instance learning methods. ...
In contrast to previous approaches to gradient-based tree learning, our method operates in the incremental learning setting rather than the batch learning setting, and does not make use of soft splits ...
Multi-Instance Learning The evaluation metric used for multi-instance learning is the 10-fold cross-validation accuracy. ...
arXiv:1901.07777v3
fatcat:ecs4omwtkbg3lni7umr7tc56nu
A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy
2010
Algorithms
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The experimental results obtained from iteratively applying WP-SVM to improve detection sensitivity demonstrate its viability for incremental learning, thereby motivating further follow on research to ...
Incremental data are incorporated in the WP-SVM as a weighted vector space, and the only storage requirements are the hyperplane parameters. ...
To the best of our knowledge, no prior work has addressed incremental multi-classification of polyps using a support vector machine (SVM) approach within the framework of dynamic learning. ...
doi:10.3390/a3010001
fatcat:a76fwwu6nnajxcjmms2gzosuk4
Incremental multi-classifier learning algorithm on grid'5000 for large scale image annotation
2010
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval - VLS-MCMR '10
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In this paper, we proposed an incremental multi-classifier (SVM classifiers were used) learning algorithm for large scale imbalanced image annotation. ...
With our previous research, active learning with multi-classifier showed considering performance in large scale data but much calculation was involved. ...
PARALLEL INCREMENTAL MULTI-CLASSIFIER LEARNING ALGORITHM
Incremental Multi-classifier Learning Algorithm For solving the large scale imbalanced data, active learning with multi-classifier was adopted ...
doi:10.1145/1878137.1878139
fatcat:xugz6e7lavasdjdcpjy37sfhbi
Flexible Workshop Scheduling Optimization Based On Multi-agent Technology
2016
International Journal of Hybrid Information Technology
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This paper proposed an algorithm which was a combination of the ant colony algorithm and Q-learning algorithm. ...
Considering the complexity of flexible workshop scheduling, combined with plant production process characteristics and constraints, we constructed a multi-agent system model to solve multi-objective flexible ...
Q-Learning Algorithm Q-learning is one of the main algorithms, which was firstly proposed by Watkins in his doctoral dissertation in 1989 [10] . ...
doi:10.14257/ijhit.2016.9.5.25
fatcat:onbehq7mpbdxxejcxdoe345fhi
Learning without Memorizing
[article]
2019
arXiv
pre-print
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However, this is impractical as it increases the memory requirement at every incremental step, which makes it impossible to implement IL algorithms on edge devices with limited memory. ...
Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model. ...
Here k is the number of incremental steps. ...
arXiv:1811.08051v2
fatcat:w4rqi5g4sjhhvimar6uuzkilyi
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