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 1091–1100 of 15113 papers

TitleStatusHype
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Efficient Continuous Control with Double Actors and Regularized CriticsCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Show:102550
← PrevPage 110 of 1512Next →

Benchmark Results

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