Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 13, 2023
Various
emergencies
occur
frequently,
posing
threats
and
challenges
to
people’s
lives
social
security.
In
consequence,
the
evacuation
of
multi-Agent
has
become
a
significant
part
emergency
response
process.
However,
few
existing
works
only
focus
on
small
number
agents,
which
does
not
consider
problem
cooperation
caused
by
increase
agents
impact
emergencies.
Therefore,
framework
for
event-driven
is
proposed
in
this
paper,
includes
three
parts:
event
collection,
sending,
task
execution.
During
execution,
are
divided
into
groups
select
leader
group,
while
other
group
move
with
leader.
Then,
reinforcement
learning
algorithm
Space
Multi-Agent
Deep
Deterministic
Policy
Gradient
(SMADDPG),
used
path
planning.
addition,
state,
action
reward
based
Markov
game
designed,
an
environment
presented
as
scenario.
The
experiment
results
show
that
method
can
shorten
length
path,
improve
interoperability
between
when
occur,
provide
decision-making
reference
departments
formulate
plans.
Mathematics,
Год журнала:
2022,
Номер
10(1), С. 164 - 164
Опубликована: Янв. 5, 2022
A
Multi-Agent
Motion
Prediction
and
Tracking
method
based
on
non-cooperative
equilibrium
(MPT-NCE)
is
proposed
according
to
the
fact
that
some
multi-agent
intelligent
evolution
methods,
like
MADDPG,
lack
adaptability
facing
unfamiliar
environments,
are
unable
achieve
motion
prediction
tracking,
although
they
own
advantages
in
intelligence.
Featured
by
a
performance
discrimination
module
using
time
difference
function
together
with
random
mutation
applying
predictive
learning,
MPT-NCE
capable
of
improving
tracking
ability
agents
game
confrontation.
Two
groups
experiments
conducted
results
show
compared
MADDPG
method,
aspect
ability,
achieves
rate
at
more
than
90%,
which
23.52%
higher
increases
whole
efficiency
16.89%;
promotes
convergent
speed
11.76%
while
facilitating
target
25.85%.
The
shows
impressive
environmental
ability.
Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 13, 2023
Various
emergencies
occur
frequently,
posing
threats
and
challenges
to
people’s
lives
social
security.
In
consequence,
the
evacuation
of
multi-Agent
has
become
a
significant
part
emergency
response
process.
However,
few
existing
works
only
focus
on
small
number
agents,
which
does
not
consider
problem
cooperation
caused
by
increase
agents
impact
emergencies.
Therefore,
framework
for
event-driven
is
proposed
in
this
paper,
includes
three
parts:
event
collection,
sending,
task
execution.
During
execution,
are
divided
into
groups
select
leader
group,
while
other
group
move
with
leader.
Then,
reinforcement
learning
algorithm
Space
Multi-Agent
Deep
Deterministic
Policy
Gradient
(SMADDPG),
used
path
planning.
addition,
state,
action
reward
based
Markov
game
designed,
an
environment
presented
as
scenario.
The
experiment
results
show
that
method
can
shorten
length
path,
improve
interoperability
between
when
occur,
provide
decision-making
reference
departments
formulate
plans.
