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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 1551–1575 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
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