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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 1551–1600 of 1918 papers

TitleStatusHype
Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies—0
Recursive Backwards Q-Learning in Deterministic Environments—0
Recursive Reinforcement Learning—0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage—0
Regret Bounds for Discounted MDPs—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Regularized Q-learning—0
Regularized Q-Learning with Linear Function Approximation—0
Regularly Updated Deterministic Policy Gradient Algorithm—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
Reinforced Co-Training—0
Reinforced Deep Markov Models With Applications in Automatic Trading—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward—0
Reinforcement Causal Structure Learning on Order Graph—0
Reinforcement Evolutionary Learning Method for self-learning—0
Reinforcement learning adaptive fuzzy controller for lighting systems: application to aircraft cabin—0
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3—0
Reinforcement learning approach for resource allocation in humanitarian logistics—0
Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks—0
Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue—0
Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor—0
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks—0
Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning—0
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets—0
Reinforcement learning based local path planning for mobile robot—0
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems—0
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
Reinforcement learning based recommender systems: A survey—0
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations—0
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment—0
Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications—0
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing—0
Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments—0
Reinforcement Learning for Assignment problem—0
Reinforcement Learning for Battery Management in Dairy Farming—0
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing—0
Reinforcement Learning for Finite Space Mean-Field Type Games—0
Reinforcement Learning for Hanabi—0
Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies—0
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
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