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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 1076–1100 of 1918 papers

TitleStatusHype
Quantum deep Q learning with distributed prioritized experience replay—0
Quantum deep recurrent reinforcement learning—0
Quantum-Inspired Reinforcement Learning in the Presence of Epistemic Ambivalence—0
Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling—0
Rank-One Modified Value Iteration—0
RansomAI: AI-powered Ransomware for Stealthy Encryption—0
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Realtime Spectrum Monitoring via Reinforcement Learning -- A Comparison Between Q-Learning and Heuristic Methods—0
Real-World Offline Reinforcement Learning from Vision Language Model Feedback—0
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
Regret of exploratory policy improvement and q-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
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