# Vinay Papudesi and Manfred Huber

AI and Robotics

Oct 23, 2013 (4 years and 6 months ago)

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Vinay

Papudesi

and Manfred Huber

INTRODUCTION

Staged skill learning involves:

To Begin:

“Skills” are innate reflexes and raw representation of the
world.

The Process:

Abstract away details of learnt skills

Use these abstractions as part of a higher
-
level
representation:

Behavioural results

Affordances

Rinse and repeat

THE DEVELOPMENTAL LEARNER

State representation encodes only those
aspects of the environmental state owing
behavioural and reward implications in the
context of its current capabilities.

A compact representation

Becomes more and more abstract over time

But how to model this?...

STATE
-
SPACES

Three yummy flavours:

External (World
)

State Space

(…maps to…)

Internal State Space

(…composed of…)

Action State Spaces

Internal and External spaces are good friends:

S
i

← I(
S
e
)

Where:

Internal state

=

S
i

External state

=

S
e

Mapping function =

I

Objective:

Don’t hard
-
code mapping function, automate it!

Internal State Space is a vector of Action
S
paces, one for each action
the agent provides…

ACTION
SPACE

An action space is defined as a vector
of paired

(indicator, predicator
)

conditions.

Conditions are
-
agnostic

Can be reused for learning different tasks

Improvement over previous work

When an action is performed:

Signals a transition between internal states,
S
1

S
2
.

Observes an outcome from the world,

.

Two conditions are constructed:

Indicator:

C
ind
(S
2
) = oʹ

Predicator:

C
pre
(S
1
) = oʹ

OUTCOMES, GENETIC ALGORITHMS, NON
-
DETERMINISM, OH MY!

World state space is potentially vast

Must measure outcome somehow

Genetic Algorithms (GAs)
are used to train hierarchical,
rule
-
based, classifiers

What if an outcome cannot be accurately
measured?

Classifiers simply flag world state as non
-
deterministic.

Outcome is thus a triple type:

(success%, failure%, undetermined)

‘FIND’ ACTION

“Rotate 360
°

or until an object is visible”

With the abstract state space constructed, the
agent can now learn optimal policies for

Treat the problem as a Markov Decision Process
(MDP).

From some internal state the agent must select an
appropriate action to progress toward completing the

Reinforcement learning is used to compute such
policies:

Select the policy which maximises the expected future
return.

Future reward is estimated from prior experience.

Agent interacts with environment, recording
experiences as it does so.

The internal source and destination states get
updated with new conditions.

The reward function is re
-
computed as the average
reinforcement value over all the recorded
experiences pertaining to the chosen action.

Will eventually converge on the true model

-
SPECIFIC CONDITIONS

Not all tasks can be optimally represented with
this approach.

Actions are individually encapsulated, knowledge
contained within them is not shared among them.

E.g. ‘GOTO’ and ‘PICK’

Solution is to build ‘bipartition’ states

Allow the GOTO task a condition on whether the item
can be
PICKed
.

… but only if the reward for doing so is significant and
the condition is statistically stable (low variance) and
deterministic.

RESULTS
-

FORAGING

Left:

A hard
-
coded,
expert
-
designed
state space and
policy.

Right:

Dynamically
acquired
equivalent.

RESULTS

STATE SPACE SIZE

As the agent
interacts with the
environment the
proposed
algorithm
maintains a near
-
constant state
space complexity.

The
representation is
continually
abstracted.

RESULTS

POLICY PERFORMANCE

The presented
technique is
comparable to
manually
-
designed
behaviour.

Domain specific
models are slow to
converge.

Their state spaces
are more complex

= harder to learn.

CONCLUSIONARY

SENTIMENTS

The paper describes an approach that constructs
an abstract internal state space that is grounded
in the set of actions that the agent provides.
Reinforcement learning aids in selecting actions to

By applying an inherently epigenetic design they
have devised a developmental learner that
produces results that are comparable to hand
-
rolled solutions.

Task learning is performed in a bottom
-
up fashion
(actions to tasks), but the representation of new
tasks thereafter can be constructed from the top
-
down using previously acquired state abstractions.