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 701–750 of 15113 papers

TitleStatusHype
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route PredictionCode1
Submodular Reinforcement LearningCode1
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal ControlCode1
HIQL: Offline Goal-Conditioned RL with Latent States as ActionsCode1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop GamesCode1
Natural Actor-Critic for Robust Reinforcement Learning with Function ApproximationCode1
SafeDreamer: Safe Reinforcement Learning with World ModelsCode1
Robotic Manipulation Datasets for Offline Compositional Reinforcement LearningCode1
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control TasksCode1
Payload-Independent Direct Cost Learning for Image SteganographyCode1
RLTF: Reinforcement Learning from Unit Test FeedbackCode1
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Model-Bellman Inconsistency for Model-based Offline Reinforcement LearningCode1
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand CoresCode1
MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Automatic Truss Design with Reinforcement LearningCode1
Learning to Modulate pre-trained Models in RLCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
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