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 611620 of 15113 papers

TitleStatusHype
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement LearningCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Debiased Contrastive LearningCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
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Benchmark Results

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