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 1051–1075 of 15113 papers

TitleStatusHype
Action Space Shaping in Deep Reinforcement LearningCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Effective Diversity in Population Based Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
EDGE: Explaining Deep Reinforcement Learning PoliciesCode1
DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous ControlCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Dynamic Sparse Training for Deep Reinforcement LearningCode1
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
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Benchmark Results

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