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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 1176–1200 of 1918 papers

TitleStatusHype
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
Reinforcenment Learning-Aided NOMA Random Access: An AoI-Based Timeliness Perspective—0
A Framework of decision-relevant observability: Reinforcement Learning converges under relative ignorability—0
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization—0
Replay For Safety—0
Representation Learning for Context-Dependent Decision-Making—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
Reputation Bootstrapping for Composite Services using CP-nets—0
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Residual Q-Learning: Offline and Online Policy Customization without Value—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
Reverse Experience Replay—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Reward-Directed Score-Based Diffusion Models via q-Learning—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Risk-sensitive Reinforcement Learning—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem—0
Robbins-Monro conditions for persistent exploration learning strategies—0
Robotic Search & Rescue via Online Multi-task Reinforcement Learning—0
Robust and Data-efficient Q-learning by Composite Value-estimation—0
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