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 361–370 of 15113 papers

TitleStatusHype
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
Decoupling Representation Learning from Reinforcement LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
Datasets and Benchmarks for Offline Safe Reinforcement LearningCode2
DayDreamer: World Models for Physical Robot LearningCode2
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Benchmark Results

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