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 3381–3390 of 15113 papers

TitleStatusHype
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning—0
BertRLFuzzer: A BERT and Reinforcement Learning Based FuzzerCode0
SneakyPrompt: Jailbreaking Text-to-image Generative ModelsCode1
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Learning Diverse Risk Preferences in Population-based Self-playCode1
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL—0
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Benchmark Results

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