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 1251–1260 of 15113 papers

TitleStatusHype
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Discovering General Reinforcement Learning Algorithms with Adversarial Environment DesignCode1
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
Discriminator-Weighted Offline Imitation Learning from Suboptimal DemonstrationsCode1
DISK: Learning local features with policy gradientCode1
Advancing Multimodal Reasoning via Reinforcement Learning with Cold StartCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
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Benchmark Results

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