This is the first time artificial intelligence (AI) defeated a professional Go player. AlphaGo is a bot developed by Google that leveraged reinforcement learning and defeated a world champion at the ancient Chinese game of Go. Reinforcement Learning in actionĪn example of the reinforcement Learning in Action is AlphaGo Zero which was in the headlines in 2017. It’s a form of machine learning and therefore a branch of artificial intelligence.
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Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall, machines and software agents use reinforcement learning algorithms to determine the ideal behaviour based upon feedback from the environment. Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses.Īt the core of reinforcement learning is the concept that optimal behaviour or action is reinforced by a positive reward.
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The full code can be found here on this github link.
In the last, we will discuss the agent’s training and testing performance and the effect of hyper-parameter in the agent’s performance. We will discuss the rationale behind using the DQN and will cover the Experience Replay and Exploration-Exploitation dilemma encountered while training the Neural Network is discussed as well. Instead, the aim of this article is to get your hands dirty with some practical example of reinforcement learning and show the implementation of RL in solving real-world use cases. Reinforcement Learning is a massive topic and we are not going to cover everything here in detail. We will use Google’s Deepmind and Reinforcement Learning Implementation for this. In this article, we will cover a brief introduction to Reinforcement Learning and will learn about how to train a Deep Q-Network(DQN) agent to solve the “ Lunar Lander” Environment in OpenAI gym. AI Learning to land a Rocket Introduction
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