Partial primary reinforcement as a parameter of secondary reinforcement

Thesis (Ph.D.)--Boston UniversityThe problem of this paper is to investigate partial primary reinforcement as a possible parameter of secondary reinforcement. Although partial primary reinforcement is known to be important in many learning situations, there appears to be little systematic knowledge of its relationship to secondary reinforcement. An experiment was performed in which (1) a neutral stimulus was present on every training trial, (2) a primary reinforcer was present on only some of these trials, (3) after training was completed, a test was made for the secondary reinforcing properties of the neutral stimulus. Six independent groups of albino rats were trained in a simple runway with food as the primary reinforcer and goal box brightness as the neutral stimulus. Each group received a different number of primary reinforcements, namely, 100%, 90%, 80%, 60%, 40%, and 20%, out of one-hundred-twenty training trials. Half of the subjects were trained on a white goal box and half on a black goal box. When training was completed, the alleyway was converted to a T maze with black and white goal boxes. Neither goal box was visible to the subjects until after entrance. The animals were given twenty trials in the T maze, and the number of times they entered each goal box was tabulated. Analysis of the data revealed that the lower the percentage of reinforcement given during training, the greater were the number of entries into the training box during the test. Some characteristics of the function were: between 100% and 90% the strength of secondary reinforcement did not increase, between 90% and 80% there was a large increase, from 80% to 40% there was a further increase, and from 40% to 20% there was some decrease. It was also revealed that some subjects in the lower percentage of reinforcement groups went either to the training box or to the novel box on every test trial. Other aspects of the data were also analyzed. From this data a number of conclusions were drawn: 1. Partial primary reinforcement is a parameter of secondary reinforcement. Decrease in partial reinforcement results in an increase in secondary reinforcement various characteristics of this relationship were discussed. It was pointed out that the obtained function might be derived from two separate functions: the relationship of secondary reinforcement to the number of reinforced trials, and the relationship of secondary reinforcement to the number of non-reinforced trials. 2. The fact that some subjects went to the same box on every test trial was explained in terms of the development of strong secondary reinforcement, in the case of subjects who went to the training box, and in terms of the development of strong generalized secondary reinforcement, in the case of subjects who went to the novel box. 3. It has often been reported in the experimental literature that partially reinforced subjects show greater resistance to extinction than continuously reinforced subjects. Our findings can be applied to this phenomenon. Stimuli present during partial reinforcement are apt to acquire greater secondary reinforcing properties than those present during continuous reinforcement, and, hence, the presence of the former during extinction are able to maintain a higher frequency of responding than the presence of the latter. This hypothesis was distinguished from others offered in the literature which purport to explain the greater resistance to extinction in terms of secondary reinforcement. 4. It was pointed out that this experiment revealed a significant variable, secondary reinforcement, which might develop in studies whose training set up resembles ours. 5. Minor findings of the experiment were discussed

Tensile force and bond stress of longitudinal reinforcemenet in heavily reinforced concrete beam

This dissertation presents an experimental study related to the tensile force and bond stress of longitudinal reinforcement in heavily reinforced concrete beam. The test variables in this study include the ratio of longitudinal and shear reinforcement. The beam specimens are simply supported with two point load with 130mm wide, 230mm deep and 1800mm long. The tensile force behavior and bond stress of longitudinal reinforcement is observed at support region. From experimental and analytical analysis, all beam specimens are not encounter failure in bond at support region. The beam with higher longitudinal and shear reinforcement ratio experienced lower bond stress compared to the lower longitudinal and shear reinforcement ratio. Besides that, the tensile force at the support is increased significantly after the occurrence of the diagonal cracks. As the reinforcement in the middle beam yield, the tensile force at the support stops increasing. Additionally, a computer program developed to determine the bond stress-slip curve at the support zone by applying Second Order Runge-Kutta method. Bond stress along longitudinal reinforcement beyond the outer part of the support also examined theoretically using local bond stress-slip model that modified from CEB-FIP Model Code 1990

Reinforcement Learning: A Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file