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 2711–2720 of 15113 papers

TitleStatusHype
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Safe Reinforcement Learning in Tensor Reproducing Kernel Hilbert Space—0
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk—0
Optimal Attack and Defense for Reinforcement LearningCode0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Predictable Reinforcement Learning Dynamics through Entropy Rate MinimizationCode0
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning—0
Unveiling the Implicit Toxicity in Large Language ModelsCode1
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
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Benchmark Results

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