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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 1401–1450 of 1918 papers

TitleStatusHype
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning—0
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control—0
Optimal Transport-Assisted Risk-Sensitive Q-Learning—0
Optimal Use of Experience in First Person Shooter Environments—0
Optimal variance-reduced stochastic approximation in Banach spaces—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Optimistic Q-learning for average reward and episodic reinforcement learning—0
Optimization of anemia treatment in hemodialysis patients via reinforcement learning—0
Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment—0
Optimized Resource Allocation for Cloud-Native 6G Networks: Zero-Touch ML Models in Microservices-based VNF Deployments—0
Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning—0
Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes—0
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies—0
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping—0
Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks—0
ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G—0
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization—0
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
Parameterized Reinforcement Learning for Optical System Optimization—0
Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process—0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams—0
Periodic agent-state based Q-learning for POMDPs—0
Periodic Q-Learning—0
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies—0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms—0
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Photonic architecture for reinforcement learning—0
Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
PID Accelerated Temporal Difference Algorithms—0
Planning and Learning in Average Risk-aware MDPs—0
Planning and Learning with Stochastic Action Sets—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
Planning with RL and episodic-memory behavioral priors—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
Playing against Nature: causal discovery for decision making under uncertainty—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
Policy Learning with a Natural Language Action Space: A Causal Approach—0
Policy Tree Network—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Potential-Based Advice for Stochastic Policy Learning—0
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach—0
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