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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2431–2440 of 15113 papers

TitleStatusHype
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning—0
Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data—0
Unsupervised Transcript-assisted Video Summarization and Highlight Detection—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners—0
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning—0
SOReL and TOReL: Two Methods for Fully Offline Reinforcement LearningCode0
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control—0
SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning—0
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Benchmark Results

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