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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 401–425 of 1918 papers

TitleStatusHype
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Control-Tutored Reinforcement Learning: an application to the Herding Problem—0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning—0
Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stability—0
Convergence of Recursive Stochastic Algorithms using Wasserstein Divergence—0
Convergence Results For Q-Learning With Experience Replay—0
Convergent and Efficient Deep Q Learning Algorithm—0
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective—0
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation—0
Convert Language Model into a Value-based Strategic Planner—0
Convex Q Learning in a Stochastic Environment: Extended Version—0
Convex Q-Learning, Part 1: Deterministic Optimal Control—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Approximation of Convex Envelope Using Reinforcement Learning—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
Cooperative Deep Q-learning Framework for Environments Providing Image Feedback—0
Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Correlated Deep Q-learning based Microgrid Energy Management—0
An Attempt to Model Human Trust with Reinforcement Learning—0
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