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 4411–4420 of 15113 papers

TitleStatusHype
A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts—0
A Game Theoretic Framework for Model Based Reinforcement Learning—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
A Game Theoretic Perspective on Model-Based Reinforcement Learning—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning—0
A General Approach of Automated Environment Design for Learning the Optimal Power Flow—0
A General Family of Robust Stochastic Operators for Reinforcement Learning—0
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior—0
A General Framework for Learning Mean-Field Games—0
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Benchmark Results

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