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 76–100 of 15113 papers

TitleStatusHype
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
Steering Your Diffusion Policy with Latent Space Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—0
PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning—0
HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control—0
Reasoning with Exploration: An Entropy Perspective—0
Unsupervised Skill Discovery through Skill Regions Differentiation—0
IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards—0
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model LearningCode1
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning AttentionCode7
RL-Guided MPC for Autonomous Greenhouse Control—0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement LearningCode2
Value-Free Policy Optimization via Reward PartitioningCode0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
A Production Scheduling Framework for Reinforcement Learning Under Real-World ConstraintsCode1
StaQ it! Growing neural networks for Policy Mirror Descent—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes—0
Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion PolicyCode0
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Benchmark Results

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