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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 26–50 of 1918 papers

TitleStatusHype
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-errorCode1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
Optimistic Multi-Agent Policy GradientCode1
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised LearningCode1
Towards Robust Offline Reinforcement Learning under Diverse Data CorruptionCode1
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissionsCode1
Boosting Continuous Control with Consistency PolicyCode1
PGDQN: Preference-Guided Deep Q-NetworkCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Reasoning with Latent Diffusion in Offline Reinforcement LearningCode1
Robust Multi-Agent Reinforcement Learning with State UncertaintyCode1
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion PoliciesCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming SolverCode1
Solving Continuous Control via Q-learningCode1
Sustainable Online Reinforcement Learning for Auto-biddingCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of TrialsCode1
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