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 2761–2770 of 15113 papers

TitleStatusHype
Workflow-Guided Response Generation for Task-Oriented Dialogue—0
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations—0
An introduction to reinforcement learning for neuroscience—0
Reinforcement Learning for Solving Stochastic Vehicle Routing ProblemCode0
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
Out-of-Distribution-Aware Electric Vehicle Charging—0
Clipped-Objective Policy Gradients for Pessimistic Policy OptimizationCode0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
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Benchmark Results

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