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

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Showing 2391–2400 of 15113 papers

TitleStatusHype
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning—0
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond—0
A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning—0
Latent Guided Sampling for Combinatorial OptimizationCode0
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments—0
Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games—0
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Benchmark Results

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