In many sequential decision-making problems one is interested in minimizing an expected
cumulative cost while taking into account risk, ie, increased awareness of events of small …

We propose a general framework for entropy-regularized average-reward reinforcement
learning in Markov decision processes (MDPs). Our approach is based on extending the …

In many sequential decision-making problems we may want to manage risk by minimizing
some measure of variability in costs in addition to minimizing a standard criterion …

Training Reinforcement Learning (RL) agents in high-stakes applications might be too
prohibitive due to the risk associated to exploration. Thus, the agent can only use data …

Several authors have recently developed risk-sensitive policy gradient methods that
augment the standard expected cost minimization problem with a measure of variability in …

We provide sampling-based algorithms for optimization under a coherent-risk objective. The
class of coherent-risk measures is widely accepted in finance and operations research …

The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take
actions in order to minimize the expected value of a cost function, ie, that humans are risk …

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 …

We develop an approach for solving time‐consistent risk‐sensitive stochastic optimization
problems using model‐free reinforcement learning (RL). Specifically, we assume agents …

In real-world decision-making, uncertainty is important yet difficult to handle. Stochastic
dominance provides a theoretically sound approach to comparing uncertain quantities, but …