Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

There are multiple viewpoints that an author may take when writing a book on dynamic
programming (DP), depending on the research community that (s) he belongs to and the …

In this paper, a Reinforcement Learning (RL)-based approach to optimally dispatch PV
inverters in unbalanced distribution systems is presented. The proposed approach exploits a …

In stochastic optimal control the distribution of the exogenous noise is typically unknown and
must be inferred from limited data before dynamic programming (DP)-based solution …

We propose a new value function approach for model-free reinforcement learning in Markov
decision processes involving high dimensional states that addresses the issues of …

We consider infinite-horizon stationary $\gamma $-discounted Markov Decision Processes,
for which it is known that there exists a stationary optimal policy. Using Value and Policy …

Reinforcement learning (RL) and learning from demonstration (LfD) are two popular families
of algorithms for learning policies for sequential decision problems, but they are often …

For intelligent surveillance, the issue of person re-identification has attracted extensive
research interest due to its great academic value and broad application prospect. This issue …

Maintaining a balance between the supply and demand of products by optimizing
replenishment decisions is one of the most important challenges in the supply chain …

This article studies least-squares approximate policy iteration (API) methods with
parametrized value-function approximation. We study several variations of the policy …