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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 751–800 of 1918 papers

TitleStatusHype
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
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
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
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari GamesCode1
DDPG Learning for Aerial RIS-Assisted MU-MISO Communications—0
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing—0
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement LearningCode1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
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
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic—0
On the Learning and Learnability of QuasimetricsCode1
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
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic ExplorationCode1
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
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
Mildly Conservative Q-Learning for Offline Reinforcement LearningCode1
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
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
Introspective Experience Replay: Look Back When SurprisedCode0
Concentration bounds for SSP Q-learning for average cost MDPs—0
Balancing Profit, Risk, and Sustainability for Portfolio Management—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Offline RL for Natural Language Generation with Implicit Language Q LearningCode2
Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning—0
CoNSoLe: Convex Neural Symbolic Learning—0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
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