Summary

While online learning, which we talked about in Chapter 8, Online Learning for Time-Series is tackling traditional supervised learning, reinforcement learning tries to deal with the environment. In this chapter, I've introduced reinforcement learning concepts relevant to time-series, and we've discussed many algorithms, such as deep Q-learning and MABs.

Reinforcement learning algorithms are very useful in certain contexts like recommendations, trading, or – more generally – control scenarios. In the practice section, we implemented a recommender using MABs and a trading bot with a DQN.

In the next chapter, we'll look at case studies with time-series. Among other things, we'll look at multivariate forecasts of energy demand.