Reinforcement learning algorithms, though successful, tend to over-fit to training
environments hampering their application to the real-world. This paper proposes $\text …

In recent years, robust Markov decision processes (MDPs) have emerged as a prominent
modeling framework for dynamic decision problems affected by uncertainty. In contrast to …

Abstract Robust MDPs (RMDPs) can be used to compute policies with provable worst-case
guarantees in reinforcement learning. The quality and robustness of an RMDP solution are …

Real-world applications require RL algorithms to act safely. During learning process, it is
likely that the agent executes sub-optimal actions that may lead to unsafe/poor states of the …

We present a novel Q-learning algorithm tailored to solve distributionally robust Markov
decision problems where the corresponding ambiguity set of transition probabilities for the …

Abstract Robust Markov Decision Processes (RMDPs) intend to ensure robustness with
respect to changing or adversarial system behavior. In this framework, transitions are …

One of the main challenges in imitation learning is determining what action an agent should
take when outside the state distribution of the demonstrations. Inverse reinforcement …

Fueled by both advances in robust optimization theory and applications of reinforcement
learning, robust Markov Decision Processes (RMDPs) have gained increasing attention, due …

In this paper, we consider the policy evaluation problem in reinforcement learning with
agents on a decentralized and directed network. In order to evaluate the quality of a fixed …

Numerous real-world decision or control problems involve multiple conflicting objectives
whose relative importance (preference) is required to be weighed in different scenarios …