Deep Reinforcement Learning (DRL) is a subfield of deep learning and reinforcement learning (RL) that combines neural networks (deep learning) with reinforcement learning algorithms. DRL models learn to make sequential decisions by interacting with an environment. They use deep neural networks, typically deep Q-networks (DQNs) or policy gradients, to approximate value functions or policies, and they have been particularly successful in areas such as game playing (e.g., AlphaGo) and robotics. DRL models aim to learn optimal strategies that maximize cumulative rewards in various tasks.

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Binding Peptide Generation for MHC Class I Proteins with Deep Reinforcement Learning

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Motivation: MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed.Results: We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods.

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Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation

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Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating real and/or reactive power at the connection point. Yet existing local autonomous control scheme of SIs is based on local information without coordination, which can lead to suboptimal performance. In this paper, a deep reinforcement learning (DRL) based algorithm is developed and implemented for coordinating multiple SIs. The reward scheme of the DRL is carefully designed to ensure voltage operation limits of the grid are met with more effective utilization of SI reactive power. The proposed DRL agent for voltage control can learn its policy through interaction with massive offline simulations, and adapts to load and solar variations. The performance of the DRL agent is compared against the local autonomous control on the IEEE 37 node system with thousands of scenarios. The results show a properly trained DRL agent can intelligently coordinate different SIs for maintaining grid voltage within allowable ranges, achieving reduction of PV production curtailment, and decreasing system losses.

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DeepConf: Automating Data Center Network Topologies Management with Machine Learning

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In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often designed using heuristics that leverage domain-specific properties of the workload or hardware.In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals share design and architectural similarities. We present a framework for developing general intermediate representations of network topologies using deep learning that is amenable to solving a large class of data center problems. We develop a framework, DeepConf, that simplifies the process of configuring and training deep learning agents by using our intermediate representation to learn different tasks. To illustrate the strength of our approach, we implemented and evaluated a DeepConf-agent that tackles the data center topology augmentation problem. Our initial results are promising — DeepConf performs comparably to the optimal solution.

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