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 2861–2870 of 15113 papers

TitleStatusHype
AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
AAPO: Enhance the Reasoning Capabilities of LLMs with Advantage Momentum—0
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
ACERAC: Efficient reinforcement learning in fine time discretization—0
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Benchmark Results

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