This paper investigates model robustness in reinforcement learning (RL) via the framework
of distributionally robust Markov decision processes (RMDPs). Despite recent efforts, the …

Many reinforcement learning (RL) applications have combinatorial action spaces, where
each action is a composition of sub-actions. A standard RL approach ignores this inherent …

Textual documents serve as representations of discussions on a variety of subjects. These
discussions can vary in length and may encompass a range of events or factual information …

Learning is crucial for automated negotiation, and recent years have witnessed a
remarkable achievement in application of reinforcement learning (RL) for various …

In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem,
named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step …

Offline reinforcement learning (RL) is a promising approach for training intelligent medical
agents to learn treatment policies and assist decision making in many healthcare …

Building generally capable agents is a grand challenge for deep reinforcement learning
(RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate …

Highway merging scenarios featuring mixed traffic conditions pose significant modeling and
control challenges for connected and automated vehicles (CAVs) interacting with incoming …

There is increasing interest in data-driven approaches for recommending optimal treatment
strategies in many chronic disease management and critical care applications …

We study the risk-aware reinforcement learning (RL) problem in the episodic finite-horizon
Markov decision process with unknown transition and reward functions. In contrast to the risk …