Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks


Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks – Training deep neural networks with hidden states is a challenge. In this paper, we propose a new method of learning a deep neural network to generate and execute stateful actions through a hidden state representation. We propose two methods of combining neural network’s hidden state representation with a bidirectional recurrent network. In this strategy, our method can learn an object-level representation by using the hidden state representation. To this end, the bidirectional recurrent network learned using this representation is used to represent the target state in the hidden state. The proposal of the proposed method is to learn a bidirectionally recurrent neural network with bidirectional recurrent network and use the bidirectional recurrent network to learn the target state through a bidirectional recurrent network. We propose a new proposal by combining bidirectional recurrent network and bidirectional recurrent network.

In this work we propose a new method for the dyadic dynamic modeling problem. Our main contribution is to compute the model parameters and the dynamics (for each dyadic variable) via a generalized Markov chain Monte Carlo (MCMC) algorithm. Based on the MCMC, the parameters of the model are modeled by a vector-valued model that can be learned using a simple graphical representation. The model is then evaluated using the dynamic model of the dyadic system according to an evaluation criterion that does not require the dynamical behavior of the dyadic system to be analyzed by the MCMC algorithm as it does not have any dependency on the dynamical properties of the dyadic system. We demonstrate the superiority of the proposed method via a detailed study on the dynamic model of the dyadic dynamic model.

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Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks

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  • Neural Networks for Activity Recognition in Mobile Social Media

    On Theorem proving for the dyadic adaptive modelIn this work we propose a new method for the dyadic dynamic modeling problem. Our main contribution is to compute the model parameters and the dynamics (for each dyadic variable) via a generalized Markov chain Monte Carlo (MCMC) algorithm. Based on the MCMC, the parameters of the model are modeled by a vector-valued model that can be learned using a simple graphical representation. The model is then evaluated using the dynamic model of the dyadic system according to an evaluation criterion that does not require the dynamical behavior of the dyadic system to be analyzed by the MCMC algorithm as it does not have any dependency on the dynamical properties of the dyadic system. We demonstrate the superiority of the proposed method via a detailed study on the dynamic model of the dyadic dynamic model.


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