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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

Title	Date	Tasks	Status	Hype
Distributed Heuristic Multi-Agent Path Finding with Communication	Jun 21, 2021	Multi-Agent Path FindingQ-Learning	CodeCode Available	1
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data	Jun 14, 2021	Q-LearningReinforcement Learning (RL)	CodeCode Available	1
TempoRL: Learning When to Act	Jun 9, 2021	Continuous ControlQ-Learning	CodeCode Available	1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning	May 31, 2021	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning	May 17, 2021	Offline RLQ-Learning	CodeCode Available	1
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation	May 4, 2021	Bayesian OptimizationQ-Learning	CodeCode Available	1
Optimal Market Making by Reinforcement Learning	Apr 8, 2021	Q-Learningreinforcement-learning	CodeCode Available	1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning	Feb 16, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19	Feb 9, 2021	BenchmarkingQ-Learning	CodeCode Available	1

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