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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 51100 of 1918 papers

TitleStatusHype
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Multi-Agent Trust Region LearningCode1
Neural Interactive Collaborative FilteringCode1
Offline Reinforcement Learning with Implicit Q-LearningCode1
Distributed Heuristic Multi-Agent Path Finding with CommunicationCode1
Continuous control with deep reinforcement learningCode1
On the Learning and Learnability of QuasimetricsCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Optimistic Multi-Agent Policy GradientCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
PGDQN: Preference-Guided Deep Q-NetworkCode1
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-PerformerCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
Deep Reinforcement Learning with Double Q-learningCode1
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial DetectionCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Deep Inverse Q-learning with ConstraintsCode1
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised LearningCode1
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari GamesCode1
Gradient Temporal-Difference Learning with Regularized CorrectionsCode1
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous ControlsCode1
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion PoliciesCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Reinforcement Learning in High-frequency Market MakingCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Boosting Continuous Control with Consistency PolicyCode1
Deep Recurrent Q-Learning for Partially Observable MDPsCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
ModelicaGym: Applying Reinforcement Learning to Modelica ModelsCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
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