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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

Showing 13511400 of 1918 papers

TitleStatusHype
Show Us the Way: Learning to Manage Dialog from Demonstrations0
K-spin Hamiltonian for quantum-resolvable Markov decision processes0
Self Punishment and Reward Backfill for Deep Q-LearningCode0
Zero-Shot Learning of Text Adventure Games with Sentence-Level Semantics0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing0
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning0
Reinforcement Learning for Mixed-Integer Problems Based on MPC0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning0
Augmented Q Imitation Learning (AQIL)Code0
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition0
Learning medical triage from clinicians using Deep Q-Learning0
Robust Q-learning0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms0
Convergence of Recursive Stochastic Algorithms using Wasserstein Divergence0
Q-Learning in Regularized Mean-field Games0
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D EnvironmentsCode1
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation0
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Deep Constrained Q-learning0
Deep Reinforcement Learning with Weighted Q-Learning0
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Active Perception and Representation for Robotic Manipulation0
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Application of Deep Q-Network in Portfolio Management0
A General Framework for Learning Mean-Field Games0
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning0
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle0
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles0
Transfer Reinforcement Learning under Unobserved Contextual Information0
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts0
Deep Reinforcement Learning for FlipIt Security Game0
ConQUR: Mitigating Delusional Bias in Deep Q-learningCode0
Optimistic Exploration even with a Pessimistic InitialisationCode1
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning0
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning0
Periodic Q-Learning0
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks0
UAV Aided Search and Rescue Operation Using Reinforcement Learning0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity0
Maxmin Q-learning: Controlling the Estimation Bias of Q-learningCode1
Listwise Learning to Rank with Deep Q-Networks0
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