Deep Reinforcement Learning: Playing CartPole through Asynchronous Advantage Actor Critic (A3C)…

Deep Reinforcement Learning: Playing CartPole through Asynchronous Advantage Actor Critic (A3C) with tf.keras and eager executionBy Raymond Yuan, Software Engineering InternIn this tutorial we will learn how to train a model that is able to win at the simple game CartPole using deep reinforcement learning. We'll use tf.keras and OpenAI's gym to train an agent using a technique known as Asynchronous Advantage Actor Critic (A3C). Reinforcement learning has been receiving an enormous amount of attention, but what is it exactly? Reinforcement learning is an area of machine learning that involves agents that should take certain actions from within an environment to maximize or attain some reward.In the process, we'll build practical experience and develop intuition around the following concepts:Eager execution — Eager execution is an imperative, define-by-run interface where operations are executed immediately as they are called from Python. This makes it easier to get started with TensorFlow, and can make research and development more intuitive.

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This article is related to

machine-learning,tensorflow,eager-execution