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 671–680 of 15113 papers

TitleStatusHype
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Handling Delay in Real-Time Reinforcement LearningCode0
Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game ApproachCode0
Reinforcement Learning for Active Matter—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations—0
FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulation—0
Show:102550
← PrevPage 68 of 1512Next →

Benchmark Results

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