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 2981–2990 of 15113 papers

TitleStatusHype
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters—0
A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards—0
COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping—0
Necessary and Sufficient Oracles: Toward a Computational Taxonomy For Reinforcement Learning—0
Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
Advancing Autonomous VLM Agents via Variational Subgoal-Conditioned Reinforcement Learning—0
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning—0
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Benchmark Results

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