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 1021–1030 of 15113 papers

TitleStatusHype
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous ControlCode1
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical RepresentationsCode1
Automatic Truss Design with Reinforcement LearningCode1
DREAM: Deep Regret minimization with Advantage baselines and Model-free learningCode1
DreamShard: Generalizable Embedding Table Placement for Recommender SystemsCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Automatic Curriculum Learning through Value DisagreementCode1
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary StrategiesCode1
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Benchmark Results

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