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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 421–430 of 1918 papers

TitleStatusHype
Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility—0
On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets InitiativesCode0
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement LearningCode0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications—0
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games—0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
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