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 6576–6600 of 15113 papers

TitleStatusHype
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning—0
Learning to Share in Multi-Agent Reinforcement LearningCode1
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—0
Inherently Explainable Reinforcement Learning in Natural LanguageCode0
Unsupervised Reinforcement Learning in Multiple EnvironmentsCode0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
Feature-Attending Recurrent Modules for Generalization in Reinforcement LearningCode0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
Programmatic Reward Design by Example—0
Representation and Invariance in Reinforcement Learning—0
Reinforcing Semantic-Symmetry for Document Summarization—0
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study—0
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning—0
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement LearningCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Quantifying Multimodality in World Models—0
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning—0
Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module—0
Stochastic Actor-Executor-Critic for Image-to-Image TranslationCode1
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image RegistrationCode1
Human-Level Control through Directly-Trained Deep Spiking Q-NetworksCode1
Continual Learning In Environments With Polynomial Mixing TimesCode0
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Benchmark Results

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