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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
Using deep Q-learning to understand the tax evasion behavior of risk-averse firmsCode0
Deep Reinforcement Learning using Capsules in Advanced Game Environments0
The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios0
Deep Reinforcement Fuzzing0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Trading the Twitter Sentiment with Reinforcement Learning0
Faster Deep Q-learning using Neural Episodic Control0
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks0
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling0
Learning Gaussian Policies from Smoothed Action Value Functions0
TD Learning with Constrained Gradients0
Avoiding Catastrophic States with Intrinsic Fear0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients0
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
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes0
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