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 301–325 of 15113 papers

TitleStatusHype
Assessment of Reinforcement Learning for Macro PlacementCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
Distributional Soft Actor-Critic with Three RefinementsCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
Diffusion Actor-Critic with Entropy RegulatorCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
Foundation Policies with Hilbert RepresentationsCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
DiffMimic: Efficient Motion Mimicking with Differentiable PhysicsCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
Gradient Boosting Reinforcement LearningCode2
Graphs Meet AI Agents: Taxonomy, Progress, and Future OpportunitiesCode2
Grounding Large Language Models in Interactive Environments with Online Reinforcement LearningCode2
Dialogue Learning With Human-In-The-LoopCode2
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
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Benchmark Results

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