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 621–630 of 15113 papers

TitleStatusHype
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender SystemsCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing ConstraintCode1
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
Online Symbolic Music Alignment with Offline Reinforcement LearningCode1
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement LearningCode1
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
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Benchmark Results

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