Optimizing for the Future in Non-Stationary MDPs

Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas

Student First Author: Yes

Keywords: Non-stationary MDPs, Reinforcement Learning, Lifelong Learning

Previously Published: ICML 2020

Abstract: Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process is stationary. However, in many real-world applications, this assumption is violated, and using existing algorithms may result in a performance lag. To proactively search for a good future policy, we present a policy gradient algorithm that maximizes a forecast of future performance. This forecast is obtained by fitting a curve to the counter-factual estimates of policy performance over time, without explicitly modeling the underlying non-stationarity. The resulting algorithm amounts to a non-uniform reweighting of past data, and we observe that minimizing performance over some of the data from past episodes can be beneficial when searching for a policy that maximizes future performance. We show that our algorithm, called Prognosticator, is more robust to non-stationarity than two online adaptation techniques, on three simulated problems motivated by real-world applications.

TL;DR: Many real-world problems violate the classical MDP assumption of stationarity and require lifelong adaptation to nonstationarities. We take a step towards addressing this by building upon methods from both counterfactual reasoning and curve-fitting.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/optimizing-for-the-future-in-non-stationary/code)