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About Q-Learning and Deep Q Networks (DQN)
Q-learning and Deep Q networks (DQN) provide value-based reinforcement learning methods for solving sequential decision-making problems. Q-learning uses the Bellman equation to iteratively update Q-values towards the optimal Q* function through trials and errors without needing a model of the environment. When combined with deep neural networks as function approximators, DQN can handle continuous high-dimensional state spaces that are intractable for tabular Q-learning. DQN has been successfully applied to challenging domains like Atari games.
Detailed Information
| Author: | ['bscjjw'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 52 |
| Language: | English |
| Format: | |
| Price: | FREE |
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