Paper
Systematic Evaluation of Causal Discovery in Visual Model Based\n Reinforcement Learning
Inducing causal relationships from observations is a classic problem in\nmachine learning. Most work in causality starts from the premise that the\ncausal variables themselves are observed. However, for AI agents such as robots\ntrying to make sense of their environment, the only observables are low-level\nvariables like pixels in images. To generalize well, an agent must induce\nhigh-level variables, particularly those which are causal or are affected by\ncausal variables. A central goal for AI and causality is thus the joint\ndiscovery of abstract representations and causal structure. However, we note\nthat existing environments for studying causal induction are poorly suited for\nthis objective because they have complicated task-specific causal graphs which\nare impossible to manipulate parametrically (e.g., number of nodes, sparsity,\ncausal chain length, etc.). In this work, our goal is to facilitate research in\nlearning representations of high-level variables as well as causal structures\namong them. In order to systematically probe the ability of methods to identify\nthese variables and structures, we design a suite of benchmarking RL\nenvironments. We evaluate various representation learning algorithms from the\nliterature and find that explicitly incorporating structure and modularity in\nmodels can help causal induction in model-based reinforcement learning.\n
Authors: Ke, Nan Rosemary · Didolkar, Aniket · Mittal, Sarthak · Goyal, Anirudh · Lajoie, Guillaume · Bauer, Stefan · Rezende, Danilo · Bengio, Yoshua · Mozer, Michael · Pal, Christopher