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 4061–4070 of 15113 papers

TitleStatusHype
New Challenges in Reinforcement Learning: A Survey of Security and Privacy—0
Self-Activating Neural Ensembles for Continual Reinforcement LearningCode1
Risk-Sensitive Policy with Distributional Reinforcement LearningCode1
Transformer in Transformer as Backbone for Deep Reinforcement LearningCode1
Reinforcement Learning with Success Induced Task PrioritizationCode0
Pontryagin Optimal Control via Neural NetworksCode0
POMRL: No-Regret Learning-to-Plan with Increasing Horizons—0
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression SearchCode1
RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTCode0
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving—0
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Benchmark Results

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