  This work tackles the problem of robust zero-shot planning in non-stationary
stochastic environments. We study Markov Decision Processes (MDPs) evolving
over time and consider Model-Based Reinforcement Learning algorithms in this
setting. We make two hypotheses: 1) the environment evolves continuously with a
bounded evolution rate; 2) a current model is known at each decision epoch but
not its evolution. Our contribution can be presented in four points. 1) we
define a specific class of MDPs that we call Non-Stationary MDPs (NSMDPs). We
introduce the notion of regular evolution by making an hypothesis of
Lipschitz-Continuity on the transition and reward functions w.r.t. time; 2) we
consider a planning agent using the current model of the environment but
unaware of its future evolution. This leads us to consider a worst-case method
where the environment is seen as an adversarial agent; 3) following this
approach, we propose the Risk-Averse Tree-Search (RATS) algorithm, a zero-shot
Model-Based method similar to Minimax search; 4) we illustrate the benefits
brought by RATS empirically and compare its performance with reference
Model-Based algorithms.
