SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 15051–15100 of 15113 papers

TitleStatusHype
Reinforcement Learning of Question-Answering Dialogue Policies for Virtual Museum Guides—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Policy Gradients with Variance Related Risk Criteria—0
Monte Carlo Bayesian Reinforcement Learning—0
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods—0
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink PaintingCode1
Off-Policy Actor-CriticCode0
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search—0
Evaluation of Online Dialogue Policy Learning Techniques—0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
Multi-timescale Nexting in a Reinforcement Learning Robot—0
Selecting the State-Representation in Reinforcement Learning—0
Policy Gradient Coagent Networks—0
MAP Inference for Bayesian Inverse Reinforcement Learning—0
Blending Autonomous Exploration and Apprenticeship Learning—0
A Reinforcement Learning Theory for Homeostatic Regulation—0
Action-Gap Phenomenon in Reinforcement Learning—0
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery—0
Analysis and Improvement of Policy Gradient Estimation—0
Reinforcement Learning using Kernel-Based Stochastic Factorization—0
Optimal Reinforcement Learning for Gaussian Systems—0
Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesCode0
Transfer from Multiple MDPs—0
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints—0
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement LearningCode2
Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains—0
Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories—0
Feature Construction for Inverse Reinforcement Learning—0
Linear Complementarity for Regularized Policy Evaluation and Improvement—0
Double Q-learning—0
Nonparametric Bayesian Policy Priors for Reinforcement Learning—0
LSTD with Random Projections—0
Predictive State Temporal Difference Learning—0
PAC-Bayesian Model Selection for Reinforcement Learning—0
Fast Reinforcement Learning for Energy-Efficient Wireless Communications—0
Reinforcement Learning via AIXI Approximation—0
Computational Model of Music Sight Reading: A Reinforcement Learning Approach—0
Feature Selection as a One-Player Game—0
A Generalized Natural Actor-Critic Algorithm—0
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference—0
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability—0
Solving Stochastic Games—0
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining—0
Discrete MDL Predicts in Total Variation—0
A Monte Carlo AIXI ApproximationCode0
Hebbian Learning of Bayes Optimal Decisions—0
Regularized Policy Iteration—0
Policy Search for Motor Primitives in Robotics—0
Multi-resolution Exploration in Continuous Spaces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified