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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 1001–1050 of 1918 papers

TitleStatusHype
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets—0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networks—0
Location-routing Optimisation for Urban Logistics Using Mobile Parcel Locker Based on Hybrid Q-Learning Algorithm—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates—0
Cooperative Deep Q-learning Framework for Environments Providing Image Feedback—0
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL—0
Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids—0
Multi-Agent Advisor Q-LearningCode0
Automating Control of Overestimation Bias for Reinforcement Learning—0
Can Q-Learning be Improved with Advice?—0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Can Q-learning solve Multi Armed Bantids?—0
Playing 2048 With Reinforcement LearningCode0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks—0
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Value Penalized Q-Learning for Recommender Systems—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Training Transition Policies via Distribution Matching for Complex TasksCode0
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Towards Unknown-aware Deep Q-Learning—0
Q-learning for real time control of heterogeneous microagent collectives—0
Q-Learning Scheduler for Multi-Task Learning through the use of Histogram of Task Uncertainty—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Text Generation with Efficient (Soft) Q-Learning—0
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization—0
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning—0
Value Refinement Network (VRN)—0
Robust and Data-efficient Q-learning by Composite Value-estimation—0
^2-exploration for Reinforcement Learning—0
An Attempt to Model Human Trust with Reinforcement Learning—0
Untangling Braids with Multi-agent Q-Learning—0
Unifying Top-down and Bottom-up for Recurrent Visual Attention—0
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