> ML_ALGORITHM // DEEP-Q-NETWORK-DQN_v1.0

Deep Q-Network (DQN & Rainbow)

Groundbreaking deep reinforcement learning algorithm combining Q-learning with deep convolutional networks, experience replay, and periodic target networks.

Deep Value-Based RLreinforcement-learningblack-boxlarge (>100k)

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Computational Complexity

Training Complexity:O(steps * batch_size * Q_network_forward_backward)

Inference Complexity:O(Q_network_forward)

Hardware Profile

CPU Friendly:No

Requires GPU:Yes

Memory Footprint:moderate

Interpretability & Data

Interpretability Tier:black-box

Training Data Needs:large (>100k)

Interpretability Assessment

Neural network approximates complex high-dimensional state-to-action Q-values.

Suitable Tasks & Supported Modalities

Suitable Tasks:

reinforcement learningdiscrete game control

Supported Modalities:

imagetabular

Implementing Libraries

stable-baselines3

torchrl

ray-rllib

Foundational Literature

Human-level control through deep reinforcement learning (Nature DQN)Volodymyr Mnih, Koray Kavukcuoglu (2015) · Nature

Rainbow: Combining Improvements in Deep Reinforcement LearningMatteo Hessel, Joseph Modayil (2018) · AAAI Conference on Artificial Intelligence

Common Pitfalls & Warnings

Vulnerability to the "Deadly Triad" (function approximation + bootstrapping + off-policy training) causing catastrophic divergence
Setting target network update frequency too high destroys training stability