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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 81–90 of 1918 papers

TitleStatusHype
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised LearningCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous ControlsCode1
HASCO: Towards Agile HArdware and Software CO-design for Tensor ComputationCode1
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion PoliciesCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Reinforcement Learning in High-frequency Market MakingCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
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