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Frequently Asked Questions
What is Deep Reinforcement Learning?
A method that combines deep neural networks with reinforcement learning to train agents that can master complex tasks through trial and error. Deep Reinforcement Learning (DRL) is a machine learning paradigm in which a deep neural network serves as the policy or value function in a reinforcement learning framework, enabling agents to learn directly from high-dimensional sensory inputs.
How is Deep Reinforcement Learning used in practice?
DRL achieved landmark results including superhuman Atari game play (DQN, 2015), defeating the world Go champion (AlphaGo, 2016), and robotic manipulation from raw pixels.
Why is Deep Reinforcement Learning important in AI?
Deep Reinforcement Learning is a foundational concept in Training Technique. A method that combines deep neural networks with reinforcement learning to train agents that can master complex tasks through trial and error.