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

Showing 481490 of 15113 papers

TitleStatusHype
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
Automatic Truss Design with Reinforcement LearningCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
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Benchmark Results

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