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About Deep Q-Network
This document discusses Deep Q-Networks (DQN), which is a reinforcement learning technique that uses neural networks to approximate optimal action selection policies. DQN combines deep learning and reinforcement learning to effectively operate in environments with large state spaces, like video games. The document explains how DQN works using concepts like replay memory, target networks, and epsilon-greedy policies to balance exploration and exploitation during training.
Detailed Information
| Author: | ['aishika.ranjan2021'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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