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

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Showing 2051–2075 of 15113 papers

TitleStatusHype
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
An Application of Deep Reinforcement Learning to Algorithmic TradingCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Compile Scene Graphs with Reinforcement LearningCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Concise Reasoning via Reinforcement LearningCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement LearningCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Combining Modular Skills in Multitask LearningCode1
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Benchmark Results

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