Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL relative to existing methods is their ability optimize uncertain systems independently explicit assumption process uncertainty. Recent focus on engineering applications has been directed towards development safe controllers. Previous works proposed approaches account for c...

Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maxi...

Chemical process optimization and control are affected by (1) plant-model mismatch, (2) disturbances, (3) constraints for safe operation. Reinforcement learning policy would be a natural way to solve this due its ability address stochasticity, directly account the effect of future uncertainty feedback in proper closed-loop manner; all without need an inner loop. One main reasons why reinforceme...

Existing electricity market designs assume risk neutrality and lack risk-hedging instruments, which leads to suboptimal outcomes reduces the overall efficiency. This letter enables risk-trading in chance-constrained stochastic by introducing Arrow-Debreu Securities (ADS) derives a risk-averse market-clearing model with trading. To enable trading, probability space of underlying uncertainty is d...

We consider an n-player finite strategic game. The payoff vector of each player is a random vector whose distribution is not completely known. We assume that the distribution of the random payoff vector of each player belongs to a distributional uncertainty set. Using distributionally robust approach, we define a chance-constrained game with respect to the worst-case chanceconstraint. We call s...

Project scheduling problem is mainly to determine the schedule of allocating resources in order to balance the total cost and the completion time. This paper chiefly uses chance theory to introduce project scheduling problem with uncertain variables. First, two types of single-objective programming models with uncertain variables as uncertain chance-constrained model and uncertain maximization ...