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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 551–575 of 1918 papers

TitleStatusHype
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks—0
β-DQN: Improving Deep Q-Learning By Evolving the Behavior—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Bayesian Risk-Averse Q-Learning with Streaming Observations—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading—0
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Pretrain Soft Q-Learning with Imperfect Demonstrations—0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Designing Rewards for Fast Learning—0
Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
Disentangled Planning and Control in Vision Based Robotics via Reward Machines—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Depth and nonlinearity induce implicit exploration for RL—0
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