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 4521–4530 of 15113 papers

TitleStatusHype
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations—0
Reinforcement Learning for Solving Stochastic Vehicle Routing ProblemCode0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set—0
Clipped-Objective Policy Gradients for Pessimistic Policy OptimizationCode0
Out-of-Distribution-Aware Electric Vehicle Charging—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
LLM Augmented Hierarchical Agents—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
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Benchmark Results

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