A new metaheuristic for optimal reinforcement learning algorithm exploiting a classical financial optimization equation – While machine learning (ML) models recently led to remarkable successes in many tasks, the use of ML has not been widely investigated in the reinforcement learning (RL) community. A key challenge in RL is the problem of representing the rewards of the actions as inputs to the learning algorithm, which often assumes that the RL algorithm is a continuous model that provides rewards for all actions. To alleviate this problem, we propose a novel RL algorithm with a finite set of actions. Using the RL algorithm, which is shown to be robust to adversarial inputs, we construct new RL algorithms that are able to learn to produce outputs that are qualitatively different from the inputs to the RL algorithm. Experiments on two standard benchmarks on both human and machine RL examples show that the RL algorithm compares favorably with the state of the art RL algorithms on several tasks over the time span of two years.
A number of studies have assessed the performance of crowd-sourced food price prediction. In this work, we study crowd-sourced food price prediction and propose two approaches to this problem. First, we propose a two-stage and three-stage system to predict prices in food. Second, we conduct a large-scale study to evaluate how the different types of information about each food item affect the prediction. We show that an effective and fast crowd-sourced food price prediction method is a very important tool in the field of food price prediction. We discuss the impact of different types of information, especially for a food price prediction method that uses crowdsourcing. We show that a crowd-sourced food price prediction system can provide high-quality food prices to the experts.
Computational Modeling of the Stochastic Gradient in Particle Swarm Optimization
Composite and Complexity of Fuzzy Modeling and Computation
A new metaheuristic for optimal reinforcement learning algorithm exploiting a classical financial optimization equation
Tick: an unsupervised generic generative model for image segmentation
On-Demand Crowd Sourcing for Food Price PredictionA number of studies have assessed the performance of crowd-sourced food price prediction. In this work, we study crowd-sourced food price prediction and propose two approaches to this problem. First, we propose a two-stage and three-stage system to predict prices in food. Second, we conduct a large-scale study to evaluate how the different types of information about each food item affect the prediction. We show that an effective and fast crowd-sourced food price prediction method is a very important tool in the field of food price prediction. We discuss the impact of different types of information, especially for a food price prediction method that uses crowdsourcing. We show that a crowd-sourced food price prediction system can provide high-quality food prices to the experts.