We introduce the concepts of entropy and cross‐entropy for hesitant fuzzy information, and
discuss their desirable properties. Several measure formulas are further developed, and the …

Optimal management policies for water reservoir operation are generally designed via
stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world …

Reinforcement learning (RL) allows agents to learn how to optimally interact with complex
environments. Fueled by recent advances in approximation-based algorithms, RL has …

We consider the classical policy iteration method of dynamic programming (DP), where
approximations and simulation are used to deal with the curse of dimensionality. We survey …

When people make a decision, they are usually hesitant and irresolute for one thing or
another which makes it difficult to reach a final agreement. For example, two decision …

There has been a recent focus in reinforcement learning on addressing continuous state
and action problems by optimizing parameterized policies. PI2 is a recent example of this …

This study contributes a decision analytic framework to overcome policy inertia and myopia
in complex river basin management contexts. The framework combines reservoir policy …

Emerging climate change mitigation policies focus on the implementation of global
measures relying on carbon prices to attain rapid emissions reductions, with limited …

Globally, the pressures of expanding populations, climate change, and increased energy
demands are motivating significant investments in re-operationalizing existing reservoirs or …

Policy improvement methods seek to optimize the parameters of a policy with respect to a
utility function. Owing to current trends involving searching in parameter space (rather than …