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 601–625 of 15113 papers

TitleStatusHype
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
Deep Deterministic Portfolio OptimizationCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Actor Prioritized Experience ReplayCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning StudyCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Decoupling Value and Policy for Generalization in Reinforcement LearningCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Deep Active Inference for Partially Observable MDPsCode1
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent SpaceCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
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Benchmark Results

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