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  • 2024/3/18 -Essentially, deep Q-Learning replaces the regular Q-table with the neural network. Rather than mapping a (state, action) pair to a Q-value, the neural network ...

    2024/2/27 -Deep reinforcement learning has achieved significant successes in various applications. Deep Q Network (DQN) [MKS+15] is the pioneer one. In this tutorial, we ...

    Deep Q-Network (DQN) Applied to Gymnasium Mountain Car | Python+Pytorch Deep Reinforcement Learning · Comments2.

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    2024/2/17 -Deep Q-Network (DQN): the perception system. You can think of deep Q-network as the in-depth explanation of how the perception system work. Computers can not ...

    2024/2/19 -In this paper, we propose a training algorithm based on genetic algorithm to efficiently and effectively address this critical issue. Specifically, a state ...

    2024/3/1 -Deep reinforcement learning has achieved significant successes in various applications. Deep Q Network (DQN) [MKS+15] is the pioneer one. In this tutorial, we ...

    2024/4/20 -In this paper, the state matrix corresponding to the preprocessed raster map is used as the input of the deep neural network. The deep Q network (DQN) is used ...

    2024/4/24 -This is a pytorch implementation where we will solve this environmnet using two model, DeepQNetwork and Q learning. Deep Q-Network (DQN). Hyperaparameters ...

    1日前 -This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). ... The deep Q-network ...

    2024/2/26 -Abstract. This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial ...