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 1581–1590 of 15113 papers

TitleStatusHype
Lyapunov-Regularized Reinforcement Learning for Power System Transient StabilityCode1
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic ManipulationCode1
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich TasksCode1
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement LearningCode1
MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelsCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
Market Making with Deep Reinforcement Learning from Limit Order BooksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
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Benchmark Results

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