Hands-On Reinforcement Learning with Python

Hierarchical reinforcement learning

The problem with RL is that it cannot scale well with a large number of state spaces and actions, which ultimately leads to the curse of dimensionality. Hierarchical reinforcement learning (HRL) is proposed to solve the curse of dimensionality where we decompress large problems into small subproblems in a hierarchy. Let's say the agent's goal is to reach its home from school. Here the problem is split into a set of subgoals such as going out of the school gate, booking a cab, and so on.

There are different methods used in HRL such as state-space decomposition, state abstraction, and temporal abstraction. In state-space decomposition, we decompose the state space into different subspaces and try to solve ...