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 171–180 of 15113 papers

TitleStatusHype
A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability—0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments—0
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback—0
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem—0
Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games—0
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains—0
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning—0
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Benchmark Results

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