Policy gradient methods have become a standard for training reinforcement learning agents
in a scalable and efficient manner. However, they do not account for transition uncertainty …

In robust Markov decision processes (RMDPs), it is assumed that the reward and the
transition dynamics lie in a given uncertainty set. By targeting maximal return under the most …

We study robust Markov games (RMG) with $ s $-rectangular uncertainty. We show a
general equivalence between computing a robust Nash equilibrium (RNE) of a $ s …

Entropy regularization is known to improve exploration in sequential decision-making
problems. We show that this same mechanism can also lead to nearly unbiased and lower …

Reinforcement learning (RL) is recognized as lacking generalization and robustness under
environmental perturbations, which excessively restricts its application for real-world …

Direct policy optimization in reinforcement learning is usually solved with policy-gradient
algorithms, which optimize policy parameters via stochastic gradient ascent. This paper …

Robust Reinforcement Learning tries to make predictions more robust to changes in the
dynamics or rewards of the system. This problem is particularly important when the …

Robust Markov decision processes (MDPs) aim to handle changing or partially known
system dynamics. To solve them, one typically resorts to robust optimization methods …

We introduce a new maximum entropy reinforcement learning framework based on the
distribution of states and actions visited by a policy. More precisely, an intrinsic reward …

Policy-gradient algorithms are effective reinforcement learning methods for solving control
problems with continuous state and action spaces. To compute near-optimal policies, it is …