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 526–550 of 15113 papers

TitleStatusHype
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Dataset Reset Policy Optimization for RLHFCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Learning to Manipulate Deformable Objects without DemonstrationsCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement LearningCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
DARTS: Differentiable Architecture SearchCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
Adaptive Contention Window Design using Deep Q-learningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field ExperimentsCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Curriculum Offline Imitation LearningCode1
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
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Benchmark Results

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