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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 1276–1300 of 1918 papers

TitleStatusHype
Group Equivariant Deep Reinforcement LearningCode0
Regularly Updated Deterministic Policy Gradient Algorithm—0
Gradient Temporal-Difference Learning with Regularized CorrectionsCode1
Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings—0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution—0
Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper—0
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning—0
Lookahead-Bounded Q-LearningCode0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Offline Contextual Bandits with Overparameterized ModelsCode0
Q-Learning with Differential Entropy of Q-Tables—0
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Near-Optimal Reinforcement Learning with Self-Play—0
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm—0
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
Semantic Visual Navigation by Watching YouTube VideosCode1
Q-learning with Logarithmic Regret—0
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
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