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 3651–3660 of 15113 papers

TitleStatusHype
Value of Information and Reward Specification in Active Inference and POMDPs—0
Introduction to Reinforcement Learning—0
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
Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control—0
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
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Benchmark Results

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