Deep Q-Network (DQN) is a deep reinforcement learning value-based method that combines Q-learning with deep neural networks to learn policies directly from high-dimensional sensory input. Introduced by DeepMind in 2015, DQN achieved human-level performance on atari-2600 games and established deep RL as a viable approach for complex decision-making tasks. It ...
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Deep Q-Network (DQN)
Deep Q-Network (DQN) is a deep reinforcement learning value-based method that combines Q-learning with deep neural networks to learn policies directly from high-dimensional sensory input. Introduced by DeepMind in 2015, DQN achieved human-level performance on atari-2600 games and established deep RL as a viable approach for complex decision-making tasks. It serves as a foundational algorithm referenced in policy-distillation research.
Related
- policy-distillation — DQN policies were distilled in the original Policy Distillation paper
- atari-2600 — Primary benchmark environment for DQN evaluation
- imitation-learning — Alternative paradigm to value-based RL
References
- Mnih et al. (2015). "Human-level control through deep reinforcement learning." Nature 518.