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 3161–3170 of 15113 papers

TitleStatusHype
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback—0
A Survey of Deep Reinforcement Learning in Video Games—0
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning—0
Curriculum-based Deep Reinforcement Learning for Quantum Control—0
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading—0
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery—0
Curriculum goal masking for continuous deep reinforcement learning—0
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Show:102550
← PrevPage 317 of 1512Next →

Benchmark Results

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