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 3201–3225 of 15113 papers

TitleStatusHype
Machine-learning based noise characterization and correction on neutral atoms NISQ devices—0
Learning to Modulate pre-trained Models in RLCode1
InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackCode2
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
Supervised Pretraining Can Learn In-Context Reinforcement Learning—0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Estimating player completion rate in mobile puzzle games using reinforcement learning—0
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
A Framework for dynamically meeting performance objectives on a service mesh—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
Is RLHF More Difficult than Standard RL?—0
Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams—0
Active Coverage for PAC Reinforcement Learning—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
Transferable Curricula through Difficulty Conditioned Generators—0
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement LearningCode1
MP3: Movement Primitive-Based (Re-)Planning Policy—0
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
State-wise Constrained Policy OptimizationCode1
AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing OptimizationCode0
Learning to Generate Better Than Your LLMCode1
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Benchmark Results

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