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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 851–900 of 1918 papers

TitleStatusHype
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process—0
Direct Data-Driven Discrete-time Bilinear Biquadratic Regulator—0
Goal-Conditioned Q-Learning as Knowledge DistillationCode0
Object Goal Navigation using Data Regularized Q-Learning—0
Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing—0
Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems—0
Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies—0
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling—0
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction ApproachCode0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View—0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios—0
Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes—0
DDPG Learning for Aerial RIS-Assisted MU-MISO Communications—0
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing—0
Multi-objective Optimization of Notifications Using Offline Reinforcement Learning—0
Planning with RL and episodic-memory behavioral priors—0
q-Learning in Continuous Time—0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning—0
Reinforcement Learning under Partial Observability Guided by Learned Environment Models—0
Recursive Reinforcement Learning—0
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning—0
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study—0
Visual Radial Basis Q-Network—0
RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem—0
Cooperation between Independent Market MakersCode0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
A Study of Continual Learning Methods for Q-Learning—0
Concentration bounds for SSP Q-learning for average cost MDPs—0
Introspective Experience Replay: Look Back When SurprisedCode0
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
Balancing Profit, Risk, and Sustainability for Portfolio Management—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
CoNSoLe: Convex Neural Symbolic Learning—0
Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning—0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Designing Rewards for Fast Learning—0
Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation—0
Does DQN Learn?—0
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment—0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
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