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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 41–50 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Conservative Q-Learning for Offline Reinforcement Learning	Jun 8, 2020	continuous-controlContinuous Control	CodeCode Available	1	5
Continuous control with deep reinforcement learning	Sep 9, 2015	Action Detectioncontinuous-control	CodeCode Available	1	5
Reinforcement Learning in High-frequency Market Making	Jul 14, 2024	Q-Learningreinforcement-learning	CodeCode Available	1	5
Deep Active Inference for Partially Observable MDPs	Sep 8, 2020	Deep Reinforcement LearningQ-Learning	CodeCode Available	1	5
FACMAC: Factored Multi-Agent Centralised Policy Gradients	Mar 14, 2020	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	1	5
Deep Recurrent Q-Learning for Partially Observable MDPs	Jul 23, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1	5
Boosting Continuous Control with Consistency Policy	Oct 10, 2023	continuous-controlContinuous Control	CodeCode Available	1	5
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?	Dec 1, 2020	Feature EngineeringQ-Learning	CodeCode Available	1	5
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1	5
Backprop-Free Reinforcement Learning with Active Neural Generative Coding	Jul 10, 2021	Q-Learningreinforcement-learning	CodeCode Available	1	5

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