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Deep Reinforcement Learning: Part1


Prof. Lai will introduce what is deep reinforcement learning. Deep reinforcement learning is a category of machine learning and artificial intelligence where intelligent machines can learn from their actions similar to the way humans learn from experience. Recently, Deep reinforcement learning is one of the hottest research topics, thanks to DeepMind and AlphaGo.

He uses a metaphor to explain. Deep reinforcement learning can be put as an example of a software agent and an environment. The environment provides observations and rewards to the agent. The goal of the agent is learning to perform actions to achieve maximum future reward under various observations. In other words, the software agents train to learn an optimized model in an environment by playing hundreds of millions of trial-and-errors. The famous example is ALphaGo.

Next, Prof. Lai explains the loop concept of deep reinforcement learning. And he also introduces three types of reinforcement learning:

  • model-based
  • value-based
  • policy-based

If you are interested in learning reinforcement learning, check on the see also links, you will find more information of reinforcement learning.

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