Reinforcement Learning Algorithms with Python

Efficiency

In the previous section, Choosing the appropriate algorithm, we saw that the sample efficiency between the algorithms is highly variable. Moreover, from the previous chapters, we saw that more efficient methods, such as value-based learning, still require a substantial number of interactions with the environment to learn. Maybe only model-based RL can save itself from the hunger of data. Unfortunately, model-based methods have other downsides, such as a lower performance bound.

For this reason, hybrid model-based and model-free approaches have been built. However, these are difficult to engineer and are impractical for use in real-world problems. As you can see, the efficiency-related problem is very hard to solve but at the same ...