background and purpose: active learning, combined with computer-assisted instruction, can enhance medical students’ performance and learning. this article is an attempt to introduce a new web-based approach to information technology instruction for medical students. methods: eight topics were selected to be instructed in web-based workshops. phpbb forum was used for web-based collaborative lear...

Multiple intelligences (MI), second/foreign (L2) proficiency and gender are postulated to contribute to language learning strategies (LLS). This study, first, examined whether there was any relationship between Iranian EFL learners’ LLS, on the one hand, and their MI, L2 proficiency, on the other hand. In so doing, it explored the relationship of the overall L2 strategy as well as individual st...

XCS is a complex machine learning technique that combines credit apportionment techniques for rule evaluation with genetic algorithms for rule discovery to evolve a distributed set of sub-solutions online. Recent research on XCS has mainly focused on achieving a better understanding of the reinforcement component, yielding several improvements to the architecture. Nonetheless, studies on the ru...

We explore the use of Evolution Strategies, a class of black box optimization algorithms, as an alternative to popular RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using hundreds to thousands of parallel workers, ES can solve 3D humanoid walking i...

Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to the heart of leading approaches to reinforcement learning (RL). For that reason, the recent result from OpenAI showing that a particular kind of evolution strategy (ES) can rival the performance of SGD-based deep RL me...

AbstractWe discuss the implementation of a learning algorithm for determining adaptation parameters in evolution strategies. As an initial test case, we consider the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy. The results from the new adaptive scheme when applied to several test fu...