Designing Societally Beneficial Reinforcement Learning Systems

Integrating game playing.

Designing Societally Beneficial Reinforcement Learning Systems  in Bir Berkeluy

Nathan Lambert, Aaron Snoswell, Sarah Dean, Thomas Krendl Gilbert, Tom Zick,     Apr 29, 2022

Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind’s work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential for real world applications of RL should also come with a healthy dose of caution - for example RL policies are well known to be vulnerable to exploitation, and methods for safe and robust policy development are an active area of research.

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. The focus of these research efforts to date has been to account for shortcomings of datasets or supervised learning practices that can harm individuals. However the unique ability of RL systems to leverage temporal feedback in learning complicates the types of risks and safety concerns that can arise.

This post expands on our recent whitepaper and research paper, where we aim to illustrate the different modalities harms can take when augmented with the temporal axis of RL. To combat these novel societal risks, we also propose a new kind of documentation for dynamic Machine Learning systems which aims to assess and monitor these risks both before and after deployment.

What’s Special About RL? A Taxonomy of Feedback

Reinforcement learning systems are often spotlighted for their ability to act in an environment, rather than passively make predictions. Other supervised machine learning systems, such as computer vision, consume data and return a prediction that can be used by some decision making rule. In contrast, the appeal of RL is in its ability to not only (a) directly model the impact of actions, but also to (b) improve policy performance automatically. These key properties of acting upon an environment, and learning within that environment can be understood as by considering the different types of feedback that come into play when an RL agent acts within an environment. We classify these feedback forms in a taxonomy of (1) Control, (2) Behavioral, and (3) Exogenous feedback. The first two notions of feedback, Control and Behavioral, are directly within the formal mathematical definition of an RL agent while Exogenous feedback is induced as the agent interacts with the broader world.

1. Control Feedback

First is control feedback - in the control systems engineering sense - where the action taken depends on the current measurements of the state of the system. RL agents choose actions based on an observed state according to a policy, which generates environmental feedback. For example, a thermostat turns on a furnace according to the current temperature measurement. Control feedback gives an agent the ability to react to unforeseen events (e.g. a sudden snap of cold weather) autonomously.  ....'