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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 376–400 of 1918 papers

TitleStatusHype
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment—0
Reinforcement Learning: Tutorial and Survey—0
An Agile Adaptation Method for Multi-mode Vehicle Communication Networks—0
Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions—0
Optimistic Q-learning for average reward and episodic reinforcement learning—0
Solving the Model Unavailable MARE using Q-Learning Algorithm—0
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization—0
PID Accelerated Temporal Difference Algorithms—0
Periodic agent-state based Q-learning for POMDPs—0
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov GamesCode0
Unified continuous-time q-learning for mean-field game and mean-field control problems—0
Robust Q-Learning for finite ambiguity setsCode0
Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets—0
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy—0
Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting—0
Two-Step Q-Learning—0
A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization—0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—0
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks—0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
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