Inverse Reinforcement Learning Application for Discrete and Continuous Environments

We show the application of inverse reinforcement learning (IRL) in discrete and continuous environments based on the apprenticeship focus. The objective is to learn a mathematical definition of a task based on trajectories made by an expert agent. To achieve this, there must be features functions that in some way describe abilities that the agent can learn. Therefore, the description of a task can be formulated as a linear combination of those functions. This learning method was applied in Open-AI gym environments to show the learning process of a task using demonstrations.