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 1251–1275 of 15113 papers

TitleStatusHype
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
Discovering General Reinforcement Learning Algorithms with Adversarial Environment DesignCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Discovering Reinforcement Learning AlgorithmsCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Concise Reasoning via Reinforcement LearningCode1
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement LearningCode1
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
Approximate information state for approximate planning and reinforcement learning in partially observed systemsCode1
Distributed Heuristic Multi-Agent Path Finding with CommunicationCode1
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary StrategiesCode1
Distributional Reinforcement Learning via Moment MatchingCode1
DittoGym: Learning to Control Soft Shape-Shifting RobotsCode1
Diverse Policy Optimization for Structured Action SpaceCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
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Benchmark Results

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