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 861–870 of 15113 papers

TitleStatusHype
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management StrategiesCode0
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?—0
Efficient Reinforcement Learning by Guiding Generalist World Models with Non-Curated Data—0
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces—0
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution—0
FetchBot: Object Fetching in Cluttered Shelves via Zero-Shot Sim2Real—0
Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement LearningCode0
Yes, Q-learning Helps Offline In-Context RL—0
Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning—0
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMsCode0
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Benchmark Results

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