Concept Formation Using Incremental Gaussian Mixture Models

Engel, Paulo Martins; Heinen, Milton Roberto

doi:10.1007/978-3-642-16687-7_21

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Interestingly, time-correlated data does not impair IGMN's performance as it does with neural networks. It is shown in the original Incremental Gaussian Mixture Model paper [Engel and Heinen 2010] that data should vary slowly (i.e., it should not be independent and identically distributed (i.i.d. ), exactly the opposite condition for neural networks).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Fast Reinforcement Learning with Incremental Gaussian Mixture Models

Pinto

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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This work presents a novel algorithm that integrates a data-efficient function approximator with reinforcement learning in continuous state spaces. An online and incremental algorithm capable of learning from a single pass through data, called Incremental Gaussian Mixture Network (IGMN), was employed as a sample-efficient function approximator for the joint state and Qvalues space, all in a single model, resulting in a concise and data-efficient algorithm, i.e., a reinforcement learning algorithm that learns from very few interactions with the environment. Results are analyzed to explain the properties of the obtained algorithm, and it is observed that the use of the IGMN function approximator brings some important advantages to reinforcement learning in relation to conventional neural networks trained by gradient descent methods.Recent works on episodic memory for reinforcement learning, such as Model-Free Episodic Control (MFEC) [Blundell et al. 2016] and Neural Episodic Control (NEC) , have introduced a framework for stable, long-term, and immediately accessible memories to be stored and used analogously to Q-tables. Those models allow for generalization through k-Nearest Neighbors (kNN) search or by the use of a Radial Basis Function (RBF) layer and a linear output layer. An issue with both methods is the rapid filling of the memory, which is dealt with by removing the least recently used entries. In [Agostinelli et al. 2019], authors propose to cluster the least recently used entry with its nearest entry, avoiding the removal of rare but important memories. That

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Section: Experiments and Resultsmentioning

confidence: 99%

Fast Reinforcement Learning with Incremental Gaussian Mixture Models

Pinto

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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“…IGMM (for Incremental Gaussian Mixture Model) [6,7] is a concept formation algorithm based on incremental unsupervised learning techniques. Its focus is the so called unsupervised incremental learning, which considers building a model describing a data flow, where each data point is just instantaneously available to the learning system.…”

Section: Incremental Gaussian Mixture Modelmentioning

confidence: 99%

Incremental feature-based mapping from sonar data using Gaussian mixture models

Heinen

Engel

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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This paper proposes a new algorithm for feature-based environment mapping where the environment is represented using multivariate Gaussian mixture models. This algorithm, which can be used either with sonar or laser range data, is able to create and maintain environment maps in real time using few memory requirements. Moreover, it does not assume that the environment is composed by linear structures and allows computing the occupancy probabilities of any map position very fast and without introducing discretization errors. The experiments performed using sonar data show that it is able to build accurate environment representations using noisy data provided by a mobile robot.

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“…The symbols in the trajectory of Figure 3 represent the ML hypothesis in each robot position, and the black arrow represents the robot starting position and direction. More details about this experiment can be found at (Engel and Heinen, 2010a).…”

Section: Concept Formation Experimentsmentioning

confidence: 99%

IGMN: An incremental connectionist approach for concept formation, reinforcement learning and robotics

Heinen

Engel

2011

jacr

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This paper demonstrates the use of a new connectionist approach, called IGMN (standing for Incremental Gaussian Mixture Network) in some state-of-the-art research problems such as incremental concept formation, reinforcement learning and robotic mapping. IGMN is inspired on recent theories about the brain, especially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some special features that are not present in most neural network models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. Through several experiments using the proposed model it is also demonstrated that IGMN learns incrementally from data flows (each data can be immediately used and discarded), it is not sensible to initialization conditions, does not require fine-tuning its configuration parameters and has a good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for concept formation and robotic tasks.

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Concept Formation Using Incremental Gaussian Mixture Models

Cited by 6 publications

References 18 publications

Fast Reinforcement Learning with Incremental Gaussian Mixture Models

Fast Reinforcement Learning with Incremental Gaussian Mixture Models

Incremental feature-based mapping from sonar data using Gaussian mixture models

IGMN: An incremental connectionist approach for concept formation, reinforcement learning and robotics

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