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 2631–2640 of 15113 papers

TitleStatusHype
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Autonomous Attack Mitigation for Industrial Control Systems—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
AlphaStar: An Evolutionary Computation Perspective—0
A Bayesian Approach to Robust Reinforcement Learning—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
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Benchmark Results

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