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 951–975 of 15113 papers

TitleStatusHype
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Hybrid Inverse Reinforcement LearningCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and SimplicityCode1
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
Accelerating Exploration with Unlabeled Prior DataCode1
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog AgentsCode1
Automatic Curriculum Learning through Value DisagreementCode1
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
CDT: Cascading Decision Trees for Explainable Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language ModelsCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner ArchitecturesCode1
Curriculum-based Asymmetric Multi-task Reinforcement LearningCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Curriculum Offline Imitation LearningCode1
Active Exploration for Inverse Reinforcement LearningCode1
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Benchmark Results

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