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Deep Learning
Deep Reinforcement Learning

Deep Reinforcement Learning

[diːp ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ]
Deep Learning
Last updated: December 9, 2024

Definition

Combination of deep learning with reinforcement learning to create agents that can learn optimal behaviors through interaction

Detailed Explanation

Deep RL combines deep neural networks with reinforcement learning algorithms to learn optimal policies directly from high-dimensional sensory inputs. The networks learn to map states to actions while maximizing expected rewards.

Use Cases

Game playing, robotics control, autonomous systems, resource management

Related Terms

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Mathematical functions that determine the output of a neural network node based on its input

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Neural networks designed to work with sequential data by maintaining an internal state or memory

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