beta

Specifies the degree to which previous experiences (v-values) are considered in the decision function, in other words, how much weight is placed on the v-values in the decision function. A low value for beta means high exploration.

The value of the parameter beta is used in the decision function. See also mu.

The parameter beta can be either a single value, in which case the same weight is placed on all v-values, or specified per element-behavior pair.

Syntax

beta = e1->b1: v1, e1->b2: v2, ..., en->bn: vn, default: d
beta = v1

where v1,v2,...,vn are scalar expressions.

Description

beta = e1->b1: v1, e1->b2: v2, ..., en->bn: vn, default: d sets the individual weight on

• v(e1->b1) to v1,

• v(e1->b2) to v2, …,

• v(en->bn) to v2,

and the weight for all other v-values to d.

• The specification is independent of the list order:

  beta = e1->b1:v1, e1->b2:v2, default:d

  is the same as

  beta = e1->b2:v2, default:d, e1->b1:v1.

• default need not be specified if all possible combinations element->behavior are present in the list. For example,

  stimulus_elements = e1, e2
  behaviors = b1, b2
  beta = e1->b1:v11, e1->b2:v12, e2->b1:v21, e2->b2:v22
  

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beta = v1 sets the weight for all v-values to v1.

• beta = v1 is the same as beta = default: v1.

Dependencies

• The properties stimulus_elements and behaviors must be specified before beta.

• Each stimulus element used in the specification of beta must be present in the parameter stimulus_elements.

• Each behavior used in the specification of beta must be present in the parameter behaviors.

Examples

@variables x = 1
beta = element1->behavior1: x, element1->behavior2: x+1, default:x+2

sets the weight for v(element1->behavior1) to 1 and for v(element1->behavior1) to 2, and for the remaining possible element-behavior pairs to 3.

beta = element1->behavior1: 0.5, default:0

sets the weight for v(element1->behavior1) to 0.5, and for the remaining possible element-behavior pairs to 3.

beta = 0.1

sets the weight for all v-values to 0.1.