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

Showing 13711380 of 15113 papers

TitleStatusHype
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot LearningCode1
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive SummarizationCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language ModelsCode1
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line SystemsCode1
Reinforcement Learning-powered Semantic Communication via Semantic SimilarityCode1
Active Inference for Stochastic ControlCode1
Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement LearningCode1
Robust Risk-Aware Reinforcement LearningCode1
Settling the Variance of Multi-Agent Policy GradientsCode1
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Benchmark Results

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