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 4191–4200 of 15113 papers

TitleStatusHype
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning—0
Adaptive Multi-pass Decoder for Neural Machine Translation—0
Adaptive Neural Architectures for Recommender Systems—0
Adaptive operator selection utilising generalised experience—0
Adaptive optimal training of animal behavior—0
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range—0
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Benchmark Results

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