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Found 7 papers, 1 papers with code
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate
However, an optimal coordination strategy is often non-trivial to be manually defined due to the complex nature of patrolling environments.
Reinforcement Learning (RL) Augmented Cold Start Frequency Reduction in Serverless Computing
It features serverless attributes by eliminating resource management responsibilities from developers and offers transparent and on-demand scalability of applications.
A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions
Therefore, in this paper, we investigate a model-free Recurrent RL agent for function autoscaling and compare it against the model-free Proximal Policy Optimisation (PPO) algorithm.
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems
In this approach, the agents are trained to patrol an environment with various unknown dynamics and factors.
Blackbird's language matrices (BLMs): a new benchmark to investigate disentangled generalisation in neural networks
Current successes of machine learning architectures are based on computationally expensive algorithms and prohibitively large amounts of data.
Word associations and the distance properties of context-aware word embeddings
While they do show asymmetry of similarities, their asymmetries do not map those of human association norms.
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds.
