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 …

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 …

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

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 …

In a sequential decision-making problem, off-policy evaluation estimates the expected
cumulative reward of a target policy using logged trajectory data generated from a different …

We address the problem of computing reliable policies in reinforcement learning problems
with limited data. In particular, we compute policies that achieve good returns with high …