### TABLE 3.1. Some primary reinforcers, and the dimensions of the environment to which they are tuned.

### Table 3. Reinforcement Algorithm.

"... In PAGE 7: ...rom scratch. New user addition involves a similar process to reinforcement. We only need to retrieve the appropriate r vector and then add the new usern27s data via an equation similar to n2820n29. Table3 illustrates a reinforcement algorithm using all of the methods previously discussedn2c i.e.... In PAGE 7: ... Note the computational savings due to the availabilityofthe vectors r user and r imp from the original training processn2c see step n281n29. Note that the computational complexityofthealgorithm in Table3 is dominated by the Cholesky decomposition. Table 3.... ..."

### Table 9. Omniscientversus online reinforcements.

1998

Cited by 58

### TABLE II REINFORCEMENT LEARNING PARAMETERS

2004

Cited by 2

### Table 4: Longitudinal reinforcement of reference for the beams

"... In PAGE 13: ...m , corresponding to a reduction of around 40% of the maximum value obtained from the linear elastic analysis. Table4 shows the reinforcements resulting from the design according to the reference values. Table 4: Longitudinal reinforcement of reference for the beams... ..."

### Table 1: Reinforcement learning specification for wander- ing.

"... In PAGE 4: ... We conjectured that successful wandering involves maximising the amount of carpet in view while max- imising forward velocity. Table1 shows the state, ac- tions and reward for the reinforcement learning algc- rithm. The reward is a weighted sum of the compo- nents shown.... ..."

### Table 2. Description of variables in reinforcement learning

"... In PAGE 5: ....3. Proposed parameter Using the parameter proposed in Section 2.2, the formulation in the previous section is rewritten in the frame- work of reinforcement learning as in Table2 . This problem is equivalent to the search problem in a one-dimensional gridworld, where the numbers of look-ahead steps are arranged as a discrete array.... ..."

### Table 4: Reinforcement among different quality requirements.

2003

"... In PAGE 6: ... A decision table could help us in determining these implications. Table4 is a first attempt in defining such a decision support for trade-off analysis. For example, the table shows that a change in security (row) has a reverse effect on efficiency (column).... In PAGE 6: ... We believe that it is not necessary to define quality in terms of absolute numbers but only in terms of relative differences. Table4 thus does not show a table with absolute rankings among qualities requirements. We envision such a table to be useful for a first-cut, trade-off analysis among requirements changes.... ..."

Cited by 3

### Table I. Effective Reinforcement Thickness and Volume Fractions

1997

Cited by 1