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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

Showing 17761800 of 1918 papers

TitleStatusHype
Learning Gaussian Policies from Smoothed Action Value Functions0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients0
TD Learning with Constrained Gradients0
Avoiding Catastrophic States with Intrinsic Fear0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation0
A short variational proof of equivalence between policy gradients and soft Q learning0
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world0
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement LearningCode0
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking AgentsCode0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars0
QLBS: Q-Learner in the Black-Scholes(-Merton) WorldsCode0
Robust Deep Reinforcement Learning with Adversarial Attacks0
Assumed Density Filtering Q-learningCode0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality0
Zap Q-Learning0
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes0
Curriculum Q-Learning for Visual Vocabulary Acquisition0
A reinforcement learning algorithm for building collaboration in multi-agent systems0
Classification with Costly Features using Deep Reinforcement LearningCode0
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems0
A unified decision making framework for supply and demand management in microgrid networks0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms0
The Effects of Memory Replay in Reinforcement LearningCode0
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations0
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