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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 51–100 of 1918 papers

TitleStatusHype
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning—0
Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control—0
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis—0
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise—0
Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment—0
Planning and Learning in Average Risk-aware MDPs—0
Deep Q-Learning with Gradient Target Tracking—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Multi-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity—0
PairVDN - Pair-wise Decomposed Value FunctionsCode0
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Quantum-Inspired Reinforcement Learning in the Presence of Epistemic Ambivalence—0
Multi-Agent Inverse Q-Learning from Demonstrations—0
DO-IQS: Dynamics-Aware Offline Inverse Q-Learning for Optimal Stopping with Unknown Gain Functions—0
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles—0
POPGym Arcade: Parallel Pixelated POMDPsCode1
An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
Cycles and collusion in congestion games under Q-learning—0
Policy Learning with a Natural Language Action Space: A Causal Approach—0
Yes, Q-learning Helps Offline In-Context RL—0
Algorithmic Collusion under Observed Demand Shocks—0
Is Q-learning an Ill-posed Problem?—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms—0
Multi-Objective Reinforcement Learning for Critical Scenario Generation of Autonomous Vehicles—0
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Evolution of cooperation in a bimodal mixture of conditional cooperatorsCode0
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency PolicyCode3
Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks—0
Seasonal Station-Keeping of Short Duration High Altitude Balloons using Deep Reinforcement Learning—0
Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction—0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
DECAF: Learning to be Fair in Multi-agent Resource Allocation—0
VistaFlow: Photorealistic Volumetric Reconstruction with Dynamic Resolution Management via Q-Learning—0
Gap-Dependent Bounds for Federated Q-learning—0
Efficient Triangular Arbitrage Detection via Graph Neural Networks—0
Flow Q-LearningCode3
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications—0
An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions—0
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—0
Linear Q-Learning Does Not Diverge: Convergence Rates to a Bounded Set—0
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