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 3641–3650 of 15113 papers

TitleStatusHype
Autonomous Attack Mitigation for Industrial Control Systems—0
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces—0
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings—0
Deep Reinforcement Learning for Intelligent Transportation Systems—0
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey—0
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications—0
Costate-focused models for reinforcement learning—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
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Benchmark Results

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