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DQN (Deep Q-Network)
Definition:
Deep Q-Network (DQN) is a type of artificial neural network that combines deep learning with reinforcement learning algorithms to make decisions in an autonomous manner. It is often used in the field of artificial intelligence for training agents to learn and improve their decision-making skills in complex environments. DQN is particularly well-known for its ability to handle high-dimensional input spaces, making it suitable for tasks such as playing video games or optimizing processes.
The Concept of DQN (Deep Q-Network)
DQN, or Deep Q-Network, is a type of reinforcement learning algorithm used in the field of artificial intelligence. It combines deep learning techniques with Q-learning, a traditional reinforcement learning algorithm, to create a powerful model capable of learning to play complex video games and solving various control tasks.
Key Components of DQN:
Neural Networks: DQN uses deep neural networks to approximate the Q-function, which estimates the expected cumulative future rewards of taking a particular action in a given state.
Experience Replay: This technique involves storing the agent's experiences (consisting of state, action, reward, and next state) in a replay memory and sampling mini-batches during training. This improves the stability and convergence of the learning process.
Fixed Q-Targets: DQN employs two sets of neural networks - the main network and a target network. The target network is only updated periodically to provide more stable target values for the Q-learning update.
Epsilon-Greedy Strategy: To balance exploration and exploitation, DQN uses an epsilon-greedy strategy to determine whether to choose a random action or follow the learned policy.
Applications of DQN:
DQN has shown impressive performance in various domains, including playing Atari games, mastering board games like Chess and Go, autonomous driving, robotics, and more. Its ability to learn directly from raw sensory inputs makes it a versatile and widely applicable algorithm in the field of artificial intelligence.
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