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 781–790 of 15113 papers

TitleStatusHype
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningCode7
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning—0
Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems—0
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics—0
Balancing SoC in Battery Cells using Safe Action Perturbations—0
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model—0
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents—0
Zero-Shot Action Generalization with Limited Observations—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
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Benchmark Results

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