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 2951–2960 of 15113 papers

TitleStatusHype
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
Motif: Intrinsic Motivation from Artificial Intelligence FeedbackCode1
RLLTE: Long-Term Evolution Project of Reinforcement LearningCode2
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned—0
Stackelberg Batch Policy Learning—0
Robust Offline Reinforcement Learning -- Certify the Confidence Interval—0
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems—0
Tempo Adaptation in Non-stationary Reinforcement LearningCode0
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement LearningCode2
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Benchmark Results

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