Learning Causally Invariant Reward Functions from Diverse Demonstrations

In the domain of reinforcement learning, the formulation of a suitable reward function plays a pivotal role in shaping the behaviour of decision making agents. This is commonly justified by the widely adopted belief that the reward function is a succinct representation of a task goal in a given environment specified as a Markov decision process (MDP) (Ng et al., 2000). Eliciting the correct behavioural policies via the optimization of a reward function is of paramount importance for the deployment of RL agents to real world domains such as various robotics scenarios (Pomerleau, 1991; Billard et al., 2008) or expert behaviour forecasting (Kitani et al., 2012). However, the challenge of designing such a function typically entails a cumbersome and error-prone process of handcrafting a heuristic reward signal that accounts for all the intricacies of the task at hand. Inverse reinforcement learning (IRL) methods aim to solve the problem of inferring the reward function of an MDP based on a dataset of temporal behaviours.