DQN family

GPTKB entity

Statements (58)
Predicate Object
gptkbp:instance_of gptkb:Artificial_Intelligence
gptkbp:advances gptkb:machine_learning
gptkbp:applies_to reinforcement learning
gptkbp:developed_by gptkb:Volodymyr_Mnih
gptkbp:has_a_focus_on AI competitions
gptkbp:has_achieved human-level performance
gptkbp:has_inspired further research in reinforcement learning
gptkbp:has_variants gptkb:Double_DQN
gptkb:Dueling_DQN
Prioritized Experience Replay
https://www.w3.org/2000/01/rdf-schema#label DQN family
gptkbp:improves Q-learning
gptkbp:includes gptkb:Deep_Q-Networks
gptkbp:includes_techniques_like experience replay
target network
gptkbp:introduced_in gptkb:2015
gptkbp:is_a_foundation_for deep learning research
gptkbp:is_a_key_component_of AI systems
gptkbp:is_a_subject_of gptkb:academic_research
machine learning courses
gptkbp:is_associated_with exploration-exploitation trade-off
gptkbp:is_based_on value function approximation
gptkbp:is_characterized_by function approximation
off-policy learning
experience replay buffer
target network updates
gptkbp:is_cited_in numerous research papers
gptkbp:is_compared_to policy gradient methods
gptkbp:is_evaluated_by mean squared error
benchmark tasks
reward signals
gptkbp:is_implemented_in gptkb:Tensor_Flow
gptkb:Py_Torch
gptkbp:is_influenced_by human learning
gptkbp:is_known_for playing Atari games
gptkbp:is_part_of deep reinforcement learning
AI curriculum
gptkbp:is_popular_for game AI
gptkbp:is_related_to Monte Carlo methods
temporal difference learning
gptkbp:is_scalable large state spaces
gptkbp:is_used_for autonomous driving
natural language processing
healthcare applications
financial trading
gptkbp:is_used_in gptkb:robotics
real-world applications
simulated environments
multi-agent systems
game playing
gptkbp:notable_technology gptkb:Artificial_Intelligence
gptkbp:requires computational resources
large amounts of data
gptkbp:security overfitting
gptkbp:uses Q-learning
gptkbp:utilizes gptkb:neural_networks
gptkbp:bfsParent gptkb:Dueling_DQN
gptkbp:bfsLayer 6