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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 651–675 of 1918 papers

TitleStatusHype
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
FedHQL: Federated Heterogeneous Q-Learning—0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
Multi-Power Level Q-Learning Algorithm for Random Access in NOMA mMTC Systems—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming SolverCode1
Contextual Conservative Q-Learning for Offline Reinforcement Learning—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Deep Spectral Q-learning with Application to Mobile Health—0
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse—0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning—0
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
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