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 1381–1390 of 15113 papers

TitleStatusHype
Knowledge-guided Open Attribute Value Extraction with Reinforcement LearningCode1
Critic Regularized RegressionCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning FrameworkCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
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Benchmark Results

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