SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1751–1800 of 1918 papers

TitleStatusHype
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning—0
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++Code0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem—0
Information Maximizing Exploration with a Latent Dynamics Model—0
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator—0
Deep Reinforcement Learning for Traffic Light Control in Vehicular NetworksCode0
Natural Gradient Deep Q-learning—0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Learning to Explore with Meta-Policy Gradient—0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback—0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes—0
Smoothed Action Value Functions for Learning Gaussian Policies—0
Q-CP: Learning Action Values for Cooperative Planning—0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Variance Reduction Methods for Sublinear Reinforcement Learning—0
Temporal Difference Models: Model-Free Deep RL for Model-Based Control—0
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments—0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
A Deep Q-Learning Agent for the L-Game with Variable Batch TrainingCode0
Monte Carlo Q-learning for General Game PlayingCode0
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays—0
Q-learning with Nearest Neighbors—0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
Using deep Q-learning to understand the tax evasion behavior of risk-averse firmsCode0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios—0
Deep Reinforcement Fuzzing—0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Trading the Twitter Sentiment with Reinforcement Learning—0
Faster Deep Q-learning using Neural Episodic Control—0
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks—0
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling—0
Learning Gaussian Policies from Smoothed Action Value Functions—0
TD Learning with Constrained Gradients—0
Avoiding Catastrophic States with Intrinsic Fear—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation—0
A short variational proof of equivalence between policy gradients and soft Q learning—0
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world—0
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 Wars—0
QLBS: Q-Learner in the Black-Scholes(-Merton) WorldsCode0
Robust Deep Reinforcement Learning with Adversarial Attacks—0
Assumed Density Filtering Q-learningCode0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes—0
Show:102550
← PrevPage 36 of 39Next →

No leaderboard results yet.