ARC320: Reinforcement Learning – The Ultimate AI - a podcast by AWS

Reinforcement Learning (RL) can be used to solve real-world problems in robotics and conversational engines without supervision. AI algorithms that observe their surroundings and learn are considered to be the ultimate forms of AI. The RL use cases shines in multi-agent scenarios where each agent reacts in real-time to the changing situation. In this session, we explain RL, the theory, and the algorithms used. We show an MXNet-based demo that will automatically learn to play a game. We use a game and show how an agent powered by MXNet takes actions to win. Initially, you notice that the agent making very little progress, but after a few dozen iterations, it can play the game better than any human being. You can generalize this to real world problems. RL is currently used today in robotics, gaming, autonomous vehicle control, spoken language systems and many more. In this talk, I will be using Amazon EC2 P2 instances, AWS deep learning AMI, MXnet deep learning framework, Amazon EBS, and Amazon S3.

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