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Epoch-incremental reinforcement learning algorithms

100%
Zajdel R.
International Journal of Applied Mathematics and Computer Science
|
2013
|
tom 23
|
nr 3
623-635
EN
In this article, a new class of the epoch-incremental reinforcement learning algorithm is proposed. In the incremental mode, the fundamental TD(0) or TD(λ) algorithm is performed and an environment model is created. In the epoch mode, on the basis of the environment model, the distances of past-active states to the terminal state are computed. These distances and the reinforcement terminal state signal are used to improve the agent policy.
2
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A strategy learning model for autonomous agents based on classification

88%
Śnieżyński B.
International Journal of Applied Mathematics and Computer Science
|
2015
|
tom 25
|
nr 3
471-482
EN
In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process.
3
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Motor control neural models and systems theory

75%
Doya K., Kimura H., Miyamura A.
International Journal of Applied Mathematics and Computer Science
|
2001
|
tom 11
|
nr 1
77-104
EN
In this paper, we introduce several system theoretic problems brought forward by recent studies on neural models of motor control. We focus our attention on three topics: (i) the cerebellum and adaptive control, (ii) reinforcement learning and the basal ganglia, and (iii) modular control with multiple models. We discuss these subjects from both neuroscience and systems theory viewpoints with the aim of promoting interplay between the two research communities.
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