Alternative ways of exploration
In this section, we will provide you with an overview of a set of alternative approaches to the exploration problem. This won’t be an exhaustive list of approaches that exist, but rather will provide an outline of the landscape.
We’re going to explore the following three approaches to exploration:
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Randomness in the policy, when stochasticity is added to the policy that we use to get samples. The method in this family is noisy networks, which we have already covered in Chapter 8.
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Count-based methods, which keep track of the number of times the agent has seen the particular state. We will check two methods: the direct counting of states and the pseudo-count method.
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Prediction-based methods, which try to predict something from the state and from the quality of the prediction. We can make judgements about the familiarity of the agent with this state...
