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 1751–1775 of 15113 papers

TitleStatusHype
ODGR: Online Dynamic Goal Recognition—0
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
Automatic Environment Shaping is the Next Frontier in RL—0
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees—0
Functional Acceleration for Policy Mirror DescentCode0
Reinforcement Learning Pair Trading: A Dynamic Scaling approachCode1
From Imitation to Refinement -- Residual RL for Precise Assembly—0
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
Should we use model-free or model-based control? A case study of battery management systems—0
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels—0
Reinforcement Learning Meets Visual OdometryCode3
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments—0
Offline Imitation Learning Through Graph Search and Retrieval—0
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning—0
Optimality theory of stigmergic collective information processing by chemotactic cells—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
Track-MDP: Reinforcement Learning for Target Tracking with Controlled Sensing—0
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—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
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
Show:102550
← PrevPage 71 of 605Next →

Benchmark Results

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