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 11276–11300 of 15113 papers

TitleStatusHype
Unsupervised Program Synthesis for Images By Sampling Without Replacement—0
Unsupervised Reinforcement Adaptation for Class-Imbalanced TextClassification—0
Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery—0
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation—0
Unsupervised Skill Discovery through Skill Regions Differentiation—0
Unsupervised state representation learning with robotic priors: a robustness benchmark—0
Unsupervised-to-Online Reinforcement Learning—0
Unsupervised Training for Neural TSP Solver—0
Unsupervised Transcript-assisted Video Summarization and Highlight Detection—0
Unsupervised Visual Attention and Invariance for Reinforcement Learning—0
Untangling Braids with Multi-agent Q-Learning—0
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents—0
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning—0
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss—0
Upside-Down Reinforcement Learning for More Interpretable Optimal Control—0
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning—0
User-Interactive Offline Reinforcement Learning—0
User-Oriented Robust Reinforcement Learning—0
User Tampering in Reinforcement Learning Recommender Systems—0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control—0
Using Chatbots to Teach Languages—0
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Benchmark Results

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