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This paper describes several new online model-free reinforcement learning (RL) algorithms. We designed three new reinforcement algorithms, namely: QV2, QVMAX, and QVMAX2, that are all based on the QV-learning algorithm, but in contrary to QV-learning, QVMAX and QVMAX2 are off-policy RL algorithms and QV2 is a new on-policy RL algorithm. We experimentally compare these algorithms to a large number...
Neural network techniques have been widely applied to areas of such as data mining, information integration and grid computing. This paper proposes a new learning algorithm based on trust region optimization theory. In the paper, the Dogleg-algorithm to obtain the valid trust region steps is presented, and a self-adjustable method with variable coefficients is given to resolve the problem of oscillatory...
Multi-robot reinforcement learning is a very challenging area due to several issues, such as large state spaces, difficulty in reward assignment, nondeterministic action selections, and difficulty in merging learned experiences from other robots. In this paper, we propose a dynamic correlation matrix based multi-Q learning (DCM-MultiQ) method for a distributed multi-robot system. A novel dynamic correlation...
This paper discussed the sparsed feed-forward neural network, namely, how to determine and delete the redundant neurons and connections in the network. To begin with, the author gives the mathematical definition of feed-forward neural network, and then introduces the partial and topological order to the sparsed algorithm and the learning algorithm of the feed-forward neural network. As a result, the...
In this paper we propose a method to implement SOM neural network in FPGA circuits: a self organized map neural network with on-chip learning algorithm. The method implies the building of a neural network by generic blocks designed in Mathworks' Simulink environment. The main characteristics of this solution are onchip learning algorithm implementation and high reconfiguration capability and operation...
A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural...
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