alpha_w

Note: Only applicable in the A-learning and the Actor-Critic mechanisms.

Specifies the learning rate for w-values (conditioned reinforcement for a stimulus element), used in the updating of w-values. See The mechanisms.

The parameter alpha_w can be set to certain values for certain particular stimulus elements. It can also be specified as a single value, in which case the same learning rate is used for all stimulus elements.

Syntax

alpha_w = e1: v1, e2: v2, ..., en: vn, default: d
alpha_w = v1

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

Description

alpha_w = e1: v1, e2: v2, ..., en->bn: vn, default: d sets the learning rate for

• w(e1) to v1,

• w(e2) to v2, …,

• w(en) to vn,

and the learning rate for all other w-values to d.

• The specification is independent of the list order:

  alpha_w = e1:v1, e2:v2, default:d

  is the same as

  alpha_w = e2:v2, default:d, e1:v1.

• default need not be specified if all stimulus elements are present in the list. For example,

  stimulus_elements = e1, e2
  alpha_w = e1:v1, e2:v2
  

---

alpha_w = v1 sets the learning rate for all w-values to v1.

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

Dependencies

• The property stimulus_elements must be specified before alpha_w.

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

• Applicable only in the A-learning and Actor-Critic mechanisms.

Examples

@variables x = 1
alpha_w = element1: x, element2: x+1, default:x+2

sets the learning rate for w(element1) to 1 and for w(element2) to 2, and for the remaining stimulus elements to 3.

alpha_w = element1: 0.5, default:0

sets the learning rate for w(element1) to 0.5, and for the remaining stimulus elements to 3.

alpha_w = 0.1

sets the learning rate for all w-values to 0.1.