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Found 4 papers, 0 papers with code
Boosting Exploration in Actor-Critic Algorithms by Incentivizing Plausible Novel States
Actor-critic (AC) algorithms are a class of model-free deep reinforcement learning algorithms, which have proven their efficacy in diverse domains, especially in solving continuous control problems.
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification
In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification.
Ranked #1 on
Malware Classification
on Microsoft Malware Classification Challenge
(Accuracy metric)
Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples with On-Policy Experience
It is comparatively more stable and sample efficient when tested on a number of continuous control tasks in MuJoCo environments.
Optimal Actor-Critic Policy with Optimized Training Datasets
Actor-critic (AC) algorithms are known for their efficacy and high performance in solving reinforcement learning problems, but they also suffer from low sampling efficiency.
