Reinforcement learning has gained valuable popularity with the relatively recent success of DeepMind’s AlphaGo method to baeat the world champion Go player. The AlphaGo method was educated in part by reinforcement learning on deep neural networks.

This style of learning is a distinct feature of machine learning from the classical supervised and unsupervised paradigms. In reinforcement learning, the network responds to environmental data (called the state) using deep neural networks, and influences the behaviour of an agent to try to optimise a reward.

This technique helps a network to learn how to play sports, such as Atari or other video games, or some other challenge that can be rewritten as a form of game. In this tutorial, a common model of reinforcement learning, I will introduce the broad principles of Q learning, and I will demonstrate how to incorporate deep Q learning in TensorFlow.

Introduction to reinforcement learning

As mentioned above, reinforcement learning consists of a few basic entities or principles. They are: an environment that creates a condition and reward, and an entity that performs actions in the given environment. In the diagram below, you see this interaction:

The task of the agent in such a setting is to analyse the state and the incentive information it receives and pick an behaviour that maximises the input it receives from the reward. The agent learns by repetitive contact with the world, or, in other words, repeated playing of the game.

In order to succeed, it is necessary for the agent to:

1. Learn the link between states, behaviour and resulting incentives

2. Determine which is the best move to pick from (1)

Implementation (1) requires defining a certain set of principles that can be used to notify (2) and (2) is referred to as the strategy of operation. One of the most common methods of applying (1) and (2) using deep Q is the Deep Q network and the epsilon-greedy policy.

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