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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 226–250 of 1918 papers

TitleStatusHype
Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts—0
Long-term Fairness in Ride-Hailing Platform—0
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN—0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment—0
An Agile Adaptation Method for Multi-mode Vehicle Communication Networks—0
Reinforcement Learning: Tutorial and Survey—0
Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions—0
Solving the Model Unavailable MARE using Q-Learning Algorithm—0
Optimistic Q-learning for average reward and episodic reinforcement learning—0
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Reinforcement Learning in High-frequency Market MakingCode1
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization—0
PID Accelerated Temporal Difference Algorithms—0
Periodic agent-state based Q-learning for POMDPs—0
Simplifying Deep Temporal Difference LearningCode3
Unified continuous-time q-learning for mean-field game and mean-field control problems—0
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov GamesCode0
Robust Q-Learning for finite ambiguity setsCode0
Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting—0
Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting MitigationCode1
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