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 1126–1150 of 15113 papers

TitleStatusHype
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
Large Neighborhood Search based on Neural Construction HeuristicsCode1
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement LearningCode1
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning StudyCode1
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
HyperNCA: Growing Developmental Networks with Neural Cellular AutomataCode1
Reward Reports for Reinforcement LearningCode1
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement LearningCode1
A Reinforcement Learning-based Volt-VAR Control Dataset and Testing EnvironmentCode1
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL DivergenceCode1
Can Question Rewriting Help Conversational Question Answering?Code1
Reinforcement learning on graphs: A surveyCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 DiabetesCode1
Federated Reinforcement Learning with Environment HeterogeneityCode1
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading DecisionsCode1
Jump-Start Reinforcement LearningCode1
Inferring Rewards from Language in ContextCode1
Value Gradient weighted Model-Based Reinforcement LearningCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
Reinforcement Learning with Action-Free Pre-Training from VideosCode1
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Benchmark Results

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