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 1751–1800 of 15113 papers

TitleStatusHype
Functional Acceleration for Policy Mirror DescentCode0
ODGR: Online Dynamic Goal Recognition—0
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
From Imitation to Refinement -- Residual RL for Precise Assembly—0
Reinforcement Learning Pair Trading: A Dynamic Scaling approachCode1
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees—0
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications—0
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health—0
Reinforcement Learning Meets Visual OdometryCode3
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels—0
Offline Imitation Learning Through Graph Search and Retrieval—0
Should we use model-free or model-based control? A case study of battery management systems—0
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments—0
Optimality theory of stigmergic collective information processing by chemotactic cells—0
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning—0
Phase Re-service in Reinforcement Learning Traffic Signal Control—0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement LearningCode1
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—0
Track-MDP: Reinforcement Learning for Target Tracking with Controlled Sensing—0
Reinforcement Learning: Tutorial and Survey—0
Learning Goal-Conditioned Representations for Language Reward ModelsCode1
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving GeneralizationCode0
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction—0
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
Random Latent Exploration for Deep Reinforcement Learning—0
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and ReviewCode2
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning—0
Chip Placement with Diffusion ModelsCode1
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement LearningCode1
Variable-Agnostic Causal Exploration for Reinforcement LearningCode1
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Deflated Dynamics Value Iteration—0
Balancing the Scales: Reinforcement Learning for Fair ClassificationCode0
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation—0
Reinforcement Learning in High-frequency Market MakingCode1
Affordance-Guided Reinforcement Learning via Visual Prompting—0
Learning to Steer Markovian Agents under Model UncertaintyCode0
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control—0
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
PID Accelerated Temporal Difference Algorithms—0
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach—0
A Review of Nine Physics Engines for Reinforcement Learning Research—0
Gradient Boosting Reinforcement LearningCode2
Token-Mol 1.0: Tokenized drug design with large language model—0
Show:102550
← PrevPage 36 of 303Next →

Benchmark Results

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