Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...
Several population-based methods (with origins in the world of evolutionary strategies and estimation-of-distribution algorithms) for black-box optimization in continuous domains ...
This paper investigates extensions of No Free Lunch (NFL) theorems to countably infinite and uncountable infinite domains. The original NFL due to Wolpert and Macready states th...
Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...