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 1701–1710 of 15113 papers

TitleStatusHype
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Off-Policy Reinforcement Learning with High Dimensional Reward—0
Introduction to Reinforcement Learning—0
Value of Information and Reward Specification in Active Inference and POMDPs—0
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object DetectionCode1
GFlowNet Training by Policy GradientsCode0
Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization—0
The Bandit Whisperer: Communication Learning for Restless Bandits—0
Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework—0
Listwise Reward Estimation for Offline Preference-based Reinforcement LearningCode1
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Benchmark Results

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