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 9451–9475 of 15113 papers

TitleStatusHype
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
Recurrent World Models Facilitate Policy Evolution—0
Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning—0
Recursive Least Squares Advantage Actor-Critic Algorithms—0
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Recursive Reinforcement Learning—0
Recursive Sparse Pseudo-input Gaussian Process SARSA—0
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Benchmark Results

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