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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

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Showing 151–200 of 1918 papers

TitleStatusHype
Continuous control with deep reinforcement learningCode1
Deep Recurrent Q-Learning for Partially Observable MDPsCode1
Playing Atari with Deep Reinforcement LearningCode1
Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour—0
Personalized Exercise Recommendation with Semantically-Grounded Knowledge TracingCode0
A Data-Ensemble-Based Approach for Sample-Efficient LQ Control of Linear Time-Varying Systems—0
ADDQ: Adaptive Distributional Double Q-LearningCode0
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
Implicit Constraint-Aware Off-Policy Correction for Offline Reinforcement Learning—0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem—0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning—0
Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons—0
Reinforcement Learning for Hanabi—0
Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model—0
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment—0
Learning to Charge More: A Theoretical Study of Collusion by Q-Learning Agents—0
BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL—0
A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging—0
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
Distributionally Robust Deep Q-LearningCode0
Reinforcement Learning for Stock Transactions—0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
OPA-Pack: Object-Property-Aware Robotic Bin Packing—0
When a Reinforcement Learning Agent Encounters Unknown Unknowns—0
Imagination-Limited Q-Learning for Offline Reinforcement Learning—0
Automatic Reward Shaping from Confounded Offline Data—0
ShiQ: Bringing back Bellman to LLMs—0
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Convert Language Model into a Value-based Strategic Planner—0
Universal Approximation Theorem for Deep Q-Learning via FBSDE System—0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows—0
A critical assessment of reinforcement learning methods for microswimmer navigation in complex flowsCode0
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
Meta-Black-Box-Optimization through Offline Q-function LearningCode0
Universal Approximation Theorem of Deep Q-Networks—0
Rank-One Modified Value Iteration—0
Dynamic and Distributed Routing in IoT Networks based on Multi-Objective Q-Learning—0
Learning Neural Control Barrier Functions from Offline Data with Conservatism—0
Q-Learning with Clustered-SMART (cSMART) Data: Examining Moderators in the Construction of Clustered Adaptive Interventions—0
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
Non-Asymptotic Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration—0
Nash Equilibrium Between Consumer Electronic Devices and DoS Attacker for Distributed IoT-enabled RSE Systems—0
A Framework of decision-relevant observability: Reinforcement Learning converges under relative ignorability—0
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