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 8026–8050 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning with Linear Function Approximation—0
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs—0
WAX-ML: A Python library for machine learning and feedback loops on streaming dataCode1
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation—0
Simplifying Deep Reinforcement Learning via Self-SupervisionCode0
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule LearningCode0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning—0
Hyperspace Neighbor Penetration Approach to Dynamic Programming for Model-Based Reinforcement Learning Problems with Slowly Changing Variables in A Continuous State Space—0
Thompson Sampling with a Mixture Prior—0
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration—0
Online Learning for Stochastic Shortest Path Model via Posterior Sampling—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents—0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
Bayesian Bellman Operators—0
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Pretrained Encoders are All You NeedCode1
Pretraining Representations for Data-Efficient Reinforcement LearningCode1
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RLCode1
Over-the-fiber Digital Predistortion Using Reinforcement Learning—0
Self-Paced Context Evaluation for Contextual Reinforcement LearningCode0
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning—0
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingCode1
Offline Inverse Reinforcement Learning—0
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Benchmark Results

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