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 1071–1080 of 15113 papers

TitleStatusHype
Bayesian Generational Population-Based TrainingCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Emergent collective intelligence from massive-agent cooperation and competitionCode1
A Distributional Perspective on Reinforcement LearningCode1
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Show:102550
← PrevPage 108 of 1512Next →

Benchmark Results

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