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 1451–1460 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
Learning to swim in potential flowCode1
Learning to Track Dynamic Targets in Partially Known EnvironmentsCode1
Accelerating Reinforcement Learning with Learned Skill PriorsCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
BabyAI 1.1Code1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
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Benchmark Results

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