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 1401–1410 of 15113 papers

TitleStatusHype
Combining Modular Skills in Multitask LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Giraffe: Using Deep Reinforcement Learning to Play ChessCode1
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical ReasoningCode1
Geometric Multimodal Contrastive Representation LearningCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
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Benchmark Results

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