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Nitro: Boosting Distributed Reinforcement Learning with Serverless Computing
Deep reinforcement learning (DRL) has demonstrated significant potential in various
applications, including gaming AI, robotics, and system scheduling. DRL algorithms …
applications, including gaming AI, robotics, and system scheduling. DRL algorithms …
[PDF][PDF] Towards Latency Efficient DRL Inference: Improving UAV Obstacle Avoidance at the Edge Through Model Compression
It is vital that autonomous Unmanned Aerial Vehicles (UAVs) are able to avoid obstacles
effectively. When avoiding such obstacles it is important that movement decision are made …
effectively. When avoiding such obstacles it is important that movement decision are made …
High-throughput Sampling, Communicating and Training for Reinforcement Learning Systems
Reinforcement Learning (RL) algorithms require large amounts of computational resources
and time to train due to the simultaneous model training and real-time interactions with …
and time to train due to the simultaneous model training and real-time interactions with …