Ordinal Inverse Reinforcement Learning Applied to Robot Learning with Small Data

Over the last decade, the ability to teach actions to robots in a user-friendly way has gained relevance, and a practical way of teaching robots a new task is to use Inverse Reinforcement Learning (IRL). In IRL, an expert teacher shows the robot a desired behaviour and an agent builds a model of the reward. The agent can also infer a policy that performs in an optimal way within the limitations of the knowledge provided to it. However, most IRL approaches assume an (almost) optimal performance of the teaching agent, which might become unpractical if the teacher is not actually an expert. In addition, most IRL focus on discrete state-action spaces that limit their applicability to certain real-world problems such as within the context of direct Policy Search (PS) reinforcement learning. Therefore, in this paper we introduce Ordinal Inverse Reinforcement Learning (OrdIRL) for continuous state variables, in which the teacher can qualitatively evaluate robot performance by selecting one among the predefined performance levels (e.g. {bad, medium, good} for three tiers of performance). Once the OrdIRL has fit an ordinal distribution to the data, we propose to use Bayesian Optimization (BO) to either gain knowledge on the inferred model (exploration) or find a policy or action that maximizes the expected reward given the prior knowledge on the reward (exploitation). In the case of large-dimensional state-action spaces, we use Dimensionality Reduction (DR) techniques and perform the BO in the latent space. Experimental results on simulation and with a robot arm show how this approach allows for learning the reward function with small data.