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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 551–560 of 1918 papers

TitleStatusHype
Model-based versus model-free feeding control and water quality monitoring for fish growth tracking in aquaculture systems—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach—0
Approximate information state based convergence analysis of recurrent Q-learning—0
Active Inference in Hebbian Learning Networks—0
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple ReuseCode0
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