Reinforced concrete beams are normally designed as under-reinforced to provide ductile behaviour at failure i.e. the tensile moment of resistance, Mt(0), is less than the moment of resistance of the compressive zone, Mc. Since it is well established that the steel in reinforced concrete beams is prone to corrosion, the residual flexural strength is normally the main concern of asset managers. H...

Jeffrey Fagen (1993, this issue) has misunderstood our main point about reinforcement as a central principle of behavior change (Gewirtz & Pelaez-Nogueras, 1992, p. 1414) and the issue of description versus explanation in the use of the objective reinforcement term or of a subjective term such as contingency expectancy. He argued that reinforcement cannot explain some findings of his and his co...

Previous approaches to multi agent reinforcement learning are either very limited or heuristic by na ture The main reason is each agent s environment continually changes because the other agents keep changing Traditional reinforcement learning algo rithms cannot properly deal with this This paper however introduces a novel general sound method for multiple reinforcement learning agents living a...

Results of several studies suggest that delivery of supplemental (social) reinforcement for stereotypy might facilitate its subsequent extinction. We examined this possibility with 9 subjects who engaged in stereotypy by including methodological refinements to ensure that (a) subjects' stereotypy was maintained in the absence of social consequences, (b) supplementary reinforcers were highly pre...

We compared two sources of behavior variability: decreased levels of reinforcement and reinforcement contingent on variability itself. In Experiment 1, four groups of rats were reinforced for different levels of response-sequence variability: one group was reinforced for low variability, two groups were reinforced for intermediate levels, and one group was reinforced for very high variability. ...

Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots. In this paper, we present TeXDYNA, an algorithm designed to solve large reinforcement learning problems with unknown structure by integrating hierarchical abstraction techniques of Hierarchical Reinforcement Learning and f...

We introduce a biologically plausible method of implementing reinforcement learning to multi-layer neural networks. The key idea is to spatially localize the synaptic modulation induced by reinforcement signals, proceeding downstream from the initial layer to the final layer. Since reinforcement signals are known to be broadcast signals in the actual brain, we need two key assumptions, inhibito...