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 3581–3590 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards—0
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
A statistical learning strategy for closed-loop control of fluid flows—0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Design and Development of Spoken Dialogue System in Indic Languages—0
Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory—0
Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback—0
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning—0
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Benchmark Results

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