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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 1511–1520 of 1918 papers

TitleStatusHype
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation—0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Self-driving scale car trained by Deep reinforcement learning—0
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles—0
Deep Reinforcement Learning for Control of Probabilistic Boolean NetworksCode0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
Encoders and Decoders for Quantum Expander Codes Using Machine Learning—0
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorchCode2
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithmsCode0
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