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 8876–8900 of 15113 papers

TitleStatusHype
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning—0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Simplifying Deep Reinforcement Learning via Self-SupervisionCode0
Thompson Sampling with a Mixture Prior—0
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule LearningCode0
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation—0
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning—0
Online Learning for Stochastic Shortest Path Model via Posterior Sampling—0
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration—0
Over-the-fiber Digital Predistortion Using Reinforcement Learning—0
Offline Inverse Reinforcement Learning—0
Self-Paced Context Evaluation for Contextual Reinforcement LearningCode0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents—0
Bayesian Bellman Operators—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Curriculum Design for Teaching via Demonstrations: Theory and ApplicationsCode0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles—0
Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement LearningCode0
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning—0
Towards Practical Credit Assignment for Deep Reinforcement Learning—0
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty—0
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation—0
Verifiable and Compositional Reinforcement Learning SystemsCode0
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Benchmark Results

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