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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 51–60 of 1918 papers

TitleStatusHype
Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles—0
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control—0
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis—0
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise—0
Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment—0
Planning and Learning in Average Risk-aware MDPs—0
Deep Q-Learning with Gradient Target Tracking—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
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