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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 241–250 of 1918 papers

TitleStatusHype
Reinforcement Learning in High-frequency Market MakingCode1
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
Simplifying Deep Temporal Difference LearningCode3
Unified continuous-time q-learning for mean-field game and mean-field control problems—0
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov GamesCode0
Robust Q-Learning for finite ambiguity setsCode0
Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting—0
Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting MitigationCode1
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