The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-to-apples
The document discusses reinforcement learning algorithms. It introduces Q-learning and the Bellman equation. It describes policy gradient methods and how the policy gradient can be estimated using trajectories sampled from the policy. It also discusses actor-critic methods that use both a policy network and value network, with the policy network optimized using the policy gradient and value networ
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