Practical AI Value Alignment Using Stories

As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally - using only a measure of task performance as feedback--can violate societal norms for acceptable behavior or cause harm. Consequently, it becomes necessary to prioritize task performance and ensure that AI actions do not have detrimental effects. Value alignment is a property of intelligent agents, wherein they solely pursue goals and activities that are non-harmful and beneficial to humans. Current approaches to value alignment largely depend on imitation learning or learning from demonstration methods. However, the dynamic nature of values makes it difficult to learn values through imitation learning-based approaches. To overcome the limitations of imitation learning-based approaches, in this work, we introduced a complementary technique in which a value-aligned prior is learned from naturally occurring stories that embody societal norms. This value-aligned prior can detect the normative and non-normative behavior of human society as well as describe the underlying social norms associated with these behaviors. To train our models, we sourced data from the children’s educational comic strip, Goofus \& Gallant. Additionally, we have built another dataset by utilizing a crowdsourcing platform. This dataset was created specifically to identify the norms or principles exhibited in the actions depicted within the comic strips. To build a normative prior model, we trained multiple machine learning models to classify natural language descriptions and visual demonstrations of situations found in the comic strip as either normative or non-normative and into different social norms. Finally, to train a value-aligned agent, we introduced a reinforcement learning-based method, in which we train an agent with two reward signals: a standard task performance reward plus a normative behavior reward. The test environment provides the standard task performance reward, while the normative behavior reward is derived from the value-aligned prior model. We show how variations on a policy shaping technique can balance these two sources of reward and produce policies that are both effective and perceived as being more normative. We test our value-alignment technique on different interactive text-based worlds; each world is designed specifically to challenge agents with a task as well as provide opportunities to deviate from the task to engage in normative and/or altruistic behavior

Causal Confusion in Imitation Learning

Behavioral cloning reduces policy learning to supervised learning by training a discriminative model to predict expert actions given observations. Such discriminative models are non-causal: the training procedure is unaware of the causal structure of the interaction between the expert and the environment. We point out that ignoring causality is particularly damaging because of the distributional shift in imitation learning. In particular, it leads to a counter-intuitive "causal misidentification" phenomenon: access to more information can yield worse performance. We investigate how this problem arises, and propose a solution to combat it through targeted interventions---either environment interaction or expert queries---to determine the correct causal model. We show that causal misidentification occurs in several benchmark control domains as well as realistic driving settings, and validate our solution against DAgger and other baselines and ablations.Comment: Published at NeurIPS 2019 9 pages, plus references and appendice

Coordinated Multi-Agent Imitation Learning

We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.Comment: International Conference on Machine Learning 201