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 1061–1070 of 15113 papers

TitleStatusHype
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM GuardrailsCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom RepresentationsCode1
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-trainingCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Automatic Truss Design with Reinforcement LearningCode1
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Benchmark Results

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