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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
A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Variance-reduced Q-learning is minimax optimal—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
"Did You Hear That?" Learning to Play Video Games from Audio Cues—0
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Feature-Based Q-Learning for Two-Player Stochastic Games—0
RSS-Based Q-Learning for Indoor UAV Navigation—0
Provably Efficient Q-Learning with Low Switching Cost—0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
SQIL: Imitation Learning via Reinforcement Learning with Sparse RewardsCode1
Prioritized Sequence Experience Replay—0
A Kernel Loss for Solving the Bellman EquationCode0
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning—0
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