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 576–600 of 15113 papers

TitleStatusHype
A2C is a special case of PPOCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement LearningCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement LearningCode1
Debiased Contrastive LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
Deep RL Agent for a Real-Time Action Strategy GameCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
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Benchmark Results

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