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 8826–8850 of 15113 papers

TitleStatusHype
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback—0
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation—0
Recall Traces: Backtracking Models for Efficient Reinforcement Learning—0
Receding Horizon Differential Dynamic Programming—0
Receding Horizon Inverse Reinforcement Learning—0
Recent Advances in Reinforcement Learning in Finance—0
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective—0
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
Recognition Method of Important Words in Korean Text based on Reinforcement Learning—0
Recommendation Fairness: From Static to Dynamic—0
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning—0
Recommendation System-based Upper Confidence Bound for Online Advertising—0
Recommending the optimal policy by learning to act from temporal data—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS—0
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Rectifying Reinforcement Learning for Reward Matching—0
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition—0
Recurrent Attention Models for Depth-Based Person Identification—0
Recurrent Control Nets for Deep Reinforcement Learning—0
Recurrent Reinforcement Learning: A Hybrid Approach—0
Recurrent Value Functions—0
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Benchmark Results

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