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Deep reinforcement learning technique combines reinforcement learning and neural network for various applications. This paper is to propose an effective lazy training method for deep reinforcement learning, especially for deep Q-network combining neural network with Q-learning to be used for the obstacle avoidance and path planning applications. The proposed method can reduce the overall training...
This paper presents the ideal approach to how to minimize the time taken by reinforcement learning to train the model. Similar to Computer vision the progress in reinforcement learning is not influenced by new ideas but mostly by the computation, large data, infrastructure and efficiency of algorithm. These 4 things only influenced the reinforcement learning RL model. How much time it will take to...
The degree of abundance of labeled training data is an important factor in determining the performance of supervised machine learning systems. However, in some applications, labeled data are either costly to collect or easily outdated, resulting in poor generalization of trained machine learners. Nonetheless, there are often related domains where large corpuses of labeled data can be easily obtained...
This paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector of task changes. In the MTPR problem, several multi-class classification tasks are sequentially given to the learning model without notifying their task categories. This implies that the learning model is supposed to detect task changes...
The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating, simple architecture, easily processing and hardware implementation. In the training phase, the disadvantage of some CMAC models with a larger fixed learning rate is the unstable phenomenon. The smaller learning rate would cause slower...
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