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 2221–2230 of 15113 papers

TitleStatusHype
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback—0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Compositional Conservatism: A Transductive Approach in Offline Reinforcement LearningCode0
Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology—0
Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand ManipulationCode0
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
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Benchmark Results

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