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 4271–4280 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Approach for the Meal Delivery Problem—0
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization—0
A Deep Reinforcement Learning Approach for Fair Traffic Signal Control—0
A Deep Reinforcement Learning Approach for Online Parcel Assignment—0
A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments—0
A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing—0
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation—0
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support—0
A Deep Reinforcement Learning Approach to Rare Event Estimation—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
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Benchmark Results

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