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2023/6/30 -Deep Q-Network (DQN) is a groundbreaking algorithm that combines deep neural networks with Q-learning for reinforcement learning tasks. Its ability to learn ...

2023/9/26 -The DQN (Deep Q-Network) algorithm was developed by DeepMind in 2015. It was able to solve a wide range of Atari games (some to superhuman level) by ...

2023/8/7 -DQN: DQN is a value-based deep reinforcement learning algorithm that maps visual input sequence to the action value functions, using a convolutional neural ...

2024/1/29 -Abstract:This paper addresses a multi-echelon inventory management problem with a complex network topology where deriving optimal ordering decisions is ...

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 ...

2023/6/14 -Deep Q-Network (DQN) combines deep neural networks with Q-learning algorithm to approximate the optimal state or action-value function of an agent in a given ...

2023/11/9 -Abstract:This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, ...

2023/12/22 -Introduction. This example shows how to train a DQN (Deep Q Networks) agent on the Cartpole environment using the TF-Agents library. Cartpole environment.

2023/5/17 -The proposed system was able to recognize dynamic hand motions by using EMG and IMU sensors to control the robot in real-time. In, the authors propose an EMG- ...