Deep Reinforcement Learning Hands-On - Second Edition

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Beyond Model-Free – Imagination

Model-based methods allow us to decrease the amount of communication with the environment by building a model of the environment and using it during training. In this chapter, we will:

• Take a brief look at the model-based methods in reinforcement learning (RL)
• Reimplement the model, described by DeepMind researchers in the paper Imagination-Augmented Agents for Deep Reinforcement Learning (https://arxiv.org/abs/1707.06203), that adds imagination to agents

Model-based methods

To begin, let's discuss the difference between the model-free approach that we have used in the book and model-based methods, including their strong and weak points and where they might be applicable.

Model-based versus model-free

In ...