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 4671–4680 of 15113 papers

TitleStatusHype
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and SmoothnessCode0
Reinforcement Learning for Node Selection in Branch-and-Bound—0
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Robust Offline Reinforcement Learning -- Certify the Confidence Interval—0
Stackelberg Batch Policy Learning—0
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned—0
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems—0
PlotMap: Automated Layout Design for Building Game WorldsCode0
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Benchmark Results

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