Building value-based reinforcement learning agent algorithms

Value-based reinforcement learning works by learning the state-value function or the action-value function in a given environment. This recipe will show you how to create and update the value function for the Maze environment to obtain an optimal policy. Learning value functions, especially in model-free RL problems where a model of the environment is not available, can prove to be quite effective, especially for RL problems with low-dimensional state space.

Upon completing this recipe, you will have an algorithm that can generate the following optimal action sequence based on value functions:

Figure 2.3 – Optimal action sequence generated by a value-based RL algorithm with state values represented through a jet color map

Let's get started.

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install numpy...