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  • 2024/5/23 -4: Implementing a Deep Q-Network from Scratch. In this section, we'll walk through the implementation of a Deep Q-Network (DQN) from scratch. By the end of ...

    2024/4/22 -At its core, a Deep Q Network is a type of artificial neural network that utilizes Q-learning to make decisions. It serves as a fundamental component in ...

    This video shows how to modify the DQN code for FrozenLake from my Deep Q-Learning video and apply it to MountainCar. Hopefully, it can you give a better ...

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    2024/4/15 -As shown in Fig. 9, the main components of DQN algorithm are the environment, Q-network with parameters θ, target network with parameters θ − , loss function ...

    2024/5/13 -A Deep Q-Network (DQN) agent solving the CartPole-v1 environment from OpenAI's Gym. Demonstrates reinforcement learning for control tasks and serves as an ...

    2024/5/19 -A Deep Q-Network is an advanced neural network architecture used in RL for approximating the Q-value function, which evaluates the quality of particular actions ...

    In this Deep Q-Learning, aka Deep Q-Network (DQN), tutorial series, we'll code up the algorithm with PyTorch and train FlappyBird.

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    2024/5/9 -This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). ... The deep Q-network ...

    2024/4/24 -This environment is one of the five gymnasium's classic control environments. The environment is stochastic in terms of its initial state, within a given ...

    2024/6/28 -The CartPole environment is a classic problem in the field of reinforcement learning, where the goal is to balance a pole on a moving cart. Deep Q-Networks (DQN) ...