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 4101–4125 of 15113 papers

TitleStatusHype
TorchBeast: A PyTorch Platform for Distributed RLCode0
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structureCode0
RL-PGO: Reinforcement Learning-based Planar Pose-Graph OptimizationCode0
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
To the Max: Reinventing Reward in Reinforcement LearningCode0
Molecular De Novo Design through Deep Reinforcement LearningCode0
Toward Causal-Aware RL: State-Wise Action-Refined Temporal DifferenceCode0
Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural LanguageCode0
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement LearningCode0
Paying Attention to Function WordsCode0
Toward Policy Explanations for Multi-Agent Reinforcement LearningCode0
RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer InteractionsCode0
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement LearningCode0
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement LearningCode0
Towards a Common Implementation of Reinforcement Learning for Multiple Robotic TasksCode0
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement LearningCode0
Reasoning and Generalization in RL: A Tool Use PerspectiveCode0
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language ModelsCode0
Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor ControlCode0
ROBEL: Robotics Benchmarks for Learning with Low-Cost RobotsCode0
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided ExplorationCode0
Towards Augmented Microscopy with Reinforcement Learning-Enhanced WorkflowsCode0
Robofriend: An Adpative Storytelling Robotic Teddy Bear - Technical ReportCode0
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis UseCode0
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and ConstraintsCode0
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Benchmark Results

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