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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2101–2125 of 15113 papers

TitleStatusHype
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Critic Regularized RegressionCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and SimplicityCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Ctrl-DNA: Controllable Cell-Type-Specific Regulatory DNA Design via Constrained RLCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Curriculum-based Asymmetric Multi-task Reinforcement LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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