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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 711–720 of 1918 papers

TitleStatusHype
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Evolution of Q Values for Deep Q Learning in Stable Baselines—0
Accelerated Target Updates for Q-learning—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Chrome Dino Run using Reinforcement Learning—0
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—0
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