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. Wildcards (*) may be used to set several values at once.
Syntax¶
alpha_w = e1: v1, e2: v2, ..., en: vn, default: d
alpha_w = v1
alpha_w = *:v1 # Same as above
where v1,v2,...,vn
and d
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 asalpha_w = default: v1
.
Dependencies¶
The property
stimulus_elements
must be specified beforealpha_w
.Each stimulus element used in the specification of
alpha_w
must be present in the parameterstimulus_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.