Studies that have assessed whether children prefer contingent reinforcement (CR) or noncontingent reinforcement (NCR) have shown that they prefer CR. Preference for CR has, however, been evaluated only under continuous reinforcement (CRF) schedules. The prevalence of intermittent reinforcement (INT) warrants an evaluation of whether preference for CR persists as the schedule of reinforcement is...

Given the effectiveness of putative escape extinction as treatment for feeding problems, it is surprising that little is known about the effects of escape as reinforcement for appropriate eating during treatment. In the current investigation, we examined the effectiveness of escape as reinforcement for mouth clean (a product measure of swallowing), escape as reinforcement for mouth clean plus e...

This paper describes a reinforcement learning method, derived from collective intelligence principles, for increasing the combined PageRank for a set of domains. This increased rank is achieved through a set of cooperating reinforcement learners that learn, through exploration, how to add links within the set of domains. We show how reinforcement learners using traditional reward functions perf...

Knowledge Representation is important issue in reinforcement learning. In this paper, we bridge the gap between reinforcement learning and knowledge representation, by providing a rich knowledge representation framework, based on normal logic programs with answer set semantics, that is capable of solving model-free reinforcement learning problems for more complex domains and exploits the domain...

Reinforcement learning is key research in automatic control, and hierarchical reinforcement learning is a good solution to the problem of the curse of dimensionality. Hierarchical reinforcement learning can only deal with discrete space, but the state and action spaces in robotic automatic control are continuous. In order to deal with continuous spaces in hierarchical reinforcement learning, we...

In this paper, we discuss situations arising with reinforcement learning algorithms, when the reinforcement is delayed. The decision to consider delayed reinforcement is typical in many applications, and we discuss some motivations for it. Then, we summarize Q-Learning, a popular algorithm to deal with delayed reinforcement, and its recent extensions to use it to learn fuzzy logic structures (F...

This paper surveys the eld 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 eld 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 environme...

Rats were trained on a modified peak-interval timing procedure in which three response levers were individually associated with different criterion durations (10, 30 and 90 s) following the onset of a tone stimulus. Delivery of response-dependent reinforcement for each duration was independent of both the responding and the delivery of reinforcement for the other durations, such that the tone s...

In this paper, an experimental and numerical study was adopted to investigate the effect geogrid soil reinforcement prestressing on the pressure settlement relation of sand bed supporting a strip foundation. The studied parameters include foundation depth and pre-stress ratio for the cases of one and two pre-stressed reinforcement layers. The study reflected that pre-stressing of soil reinforce...

Delayed prompting can produce errorless discrimination learning. There is inherent in the procedure a disparity in reinforcement density which favors unprompted over prompted responses. We used three schedules of reinforcement to investigate the impact of reinforcement probability on transfer of stimulus control. One schedule of reinforcement was equal prior to and following a prompt (CRF/CRF),...