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 761–770 of 15113 papers

TitleStatusHype
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
De novo PROTAC design using graph-based deep generative modelsCode1
Contextualized Rewriting for Text SummarizationCode1
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Constructions in combinatorics via neural networksCode1
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Benchmark Results

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