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Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
JB Lanier · Stephen McAleer · Pierre Baldi · Roy Fox
Event URL: https://openreview.net/forum?id=I19Zha9_rA6 »

Robust reinforcement learning (RL) considers the problem of learning policies that perform well in the worst case among a set of possible environment parameter values. In real-world environments, choosing the set of possible values for robust RL can be a difficult task. When that set is specified too narrowly, the agent will be left vulnerable to reasonable parameter values unaccounted for. When specified too broadly, the agent will be too cautious. In this paper, we propose Feasible Adversarial Robust RL (FARR), a novel problem formulation and objective for automatically determining the set of environment parameter values over which to be robust. FARR implicitly defines the set of feasible parameter values as those on which an agent could achieve a benchmark reward given enough training resources. By formulating this problem as a two-player zero-sum game, optimizing the FARR objective jointly produces an adversarial distribution over parameter values with feasible support and a policy robust over this feasible parameter set. We demonstrate that approximate Nash equilibria for this objective can be found using a variation of the PSRO algorithm. Furthermore, we show that an optimal agent trained with FARR is more robust to feasible adversarial parameter selection than with existing minimax, domain-randomization, and regret objectives in a parameterized gridworld and three MuJoCo control environments.

Author Information

JB Lanier (University of California Irvine)
Stephen McAleer (CMU)
Pierre Baldi (UC Irvine)
Roy Fox (UC Irvine)

[Roy Fox](http://roydfox.com/) is a postdoc at UC Berkeley working with [Ion Stoica](http://people.eecs.berkeley.edu/~istoica/) in the Real-Time Intelligent Secure Explainable lab ([RISELab](https://rise.cs.berkeley.edu/)), and with [Ken Goldberg](http://goldberg.berkeley.edu/) in the Laboratory for Automation Science and Engineering ([AUTOLAB](http://autolab.berkeley.edu/)). His research interests include reinforcement learning, dynamical systems, information theory, automation, and the connections between these fields. His current research focuses on automatic discovery of hierarchical control structures in deep reinforcement learning and in imitation learning of robotic tasks. Roy holds a MSc in Computer Science from the [Technion](http://www.cs.technion.ac.il/), under the supervision of [Moshe Tennenholtz](http://iew3.technion.ac.il/Home/Users/Moshet.phtml), and a PhD in Computer Science from the [Hebrew University](http://www.cs.huji.ac.il/), under the supervision of [Naftali Tishby](http://www.cs.huji.ac.il/~tishby/). He was an exchange PhD student with [Larry Abbott](http://www.cs.huji.ac.il/~tishby/) and [Liam Paninski](http://www.stat.columbia.edu/~liam/) at the [Center for Theoretical Neuroscience](http://www.neurotheory.columbia.edu/) at Columbia University, and a research intern at Microsoft Research.

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