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 726–750 of 15113 papers

TitleStatusHype
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement LearningCode1
State-wise Constrained Policy OptimizationCode1
Learning to Generate Better Than Your LLMCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Neural Inventory Control in Networks via Hindsight Differentiable Policy OptimizationCode1
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement LearningCode1
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-SecondCode1
Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous DrivingCode1
Policy Regularization with Dataset Constraint for Offline Reinforcement LearningCode1
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement LearningCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement LearningCode1
Decoupled Prioritized Resampling for Offline RLCode1
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RLCode1
Value Functions are Control Barrier Functions: Verification of Safe Policies using Control TheoryCode1
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline DataCode1
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-CriticCode1
For SALE: State-Action Representation Learning for Deep Reinforcement LearningCode1
Safe Offline Reinforcement Learning with Real-Time Budget ConstraintsCode1
Improving and Benchmarking Offline Reinforcement Learning AlgorithmsCode1
Learning for Edge-Weighted Online Bipartite Matching with Robustness GuaranteesCode1
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
Subequivariant Graph Reinforcement Learning in 3D EnvironmentsCode1
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
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Benchmark Results

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