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We will focus on devising particularly compact,
particularly reasonable sets of initial codes with
particularly broad practical applicability.
It may turn out that the most useful initial
languages are not traditional programming
languages similar to the FORTH-like
one from Section A, but instead
based on a handful of primitive instructions for
massively parallel cellular automata
or on a few nonlinear operations on matrix-like
data structures such as those used in
recurrent neural network research
For example, we could use the principles of
OOPS to create a non-gradient-based, near-bias-optimal
variant of the successful recurrent network
metalearner by .
It should also be of interest to study probabilistic
Speed Prior-based OOPS variants 
and to devise applications of OOPS-like methods as
components of universal reinforcement learners (Section 5.3).
In ongoing work, we are applying OOPS to the problem
of optimal trajectory planning for robotics
in a realistic physics simulation.
This involves the interesting trade-off
between comparatively fast program-composing primitives or
and time-consuming ``action primitives'',
such as stretch-arm-until-touch-sensor-input
(compare Section 4.5).
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