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 3381–3390 of 15113 papers

TitleStatusHype
Deep Apprenticeship Learning for Playing Games—0
Deep-Attack over the Deep Reinforcement Learning—0
AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information—0
Deep Bellman Hedging—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Deep reinforcement learning guided graph neural networks for brain network analysis—0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling—0
Deep Reinforcement Learning in Cryptocurrency Market Making—0
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Benchmark Results

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