The developed framework permits that the consensusability problem can be solved when the agents' models are completely unavailable. Q-learning algorithm is employed to compute the maximum consensus region and i
The huge computation overhead of the MDP formulation is solved by the prioritized Q-learning approach, which approximates one-step Q-learning in real time based on parameter sensitivity analysis. The one-step Q-learning algorithm, a reinforcement learning method, is a direct non-Bayesian approach...
For example, deep Q-learning from Demonstration (DQfD; Hester et al. 2018), which will be discussed in the next section, applied these techniques. We will also apply the techniques in our proposed algorithm, which can be viewed as an “active” extension of DQfD. 3.3 Deep Q-learning from...
It is not until 2013 that this dilemma was partially solved by Mnih et al. [2]. By combining the Q-learning algorithm with deep learning, they proposed the preliminary version of the deep Q-network (DQN) algorithm. Two years later, Mnih et al. [3] presented the normal version of DQN...
SVM algorithm [42] is used in this work as the data classifier. The model is trained by means of the SMO algorithm [30] available in Matlab’s Bioinformatics Toolbox. Multiclass classification problems are solved by applying the one-against-all method. In all data classification cases, radial...
We proposed a routing algorithm based on comprehensive link stability, which can find the most stable link between the source node and the destination node, and provide reliable and durable communication between wearable users [12]. However, some problems have not been solved yet. First of all,...
We experimentally recreated the Q-learning update formula to tackle the sparse reward problem We implemented an entire training and evaluation process, that solved the Frozen Lake environment with 100% success rate We implemented the famous epsilon-greedy algorithm in order to create a tradeoff between...
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In this paper, the zero-sum game problem for linear discrete-time Markov jump systems is solved by two novel model-free reinforcement Q-learning algorithms, on-policy Q-learning and off-policy Q-learning. Firstly, under the framework of the zero-sum game, the game-coupled algebraic Riccati ...
That is, once the augmented ARE is solved, both the feedback and feedforward terms of the control input are obtained simultaneously. Finally, a Q-learning algorithm is proposed to solve the LQT without requiring any knowledge of the augmented system dynamics. It is verified that starting from ...