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Bipedal Walker Evo
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Philip Maas
Bipedal Walker Evo
Commits
cff99083
Commit
cff99083
authored
Jan 28, 2022
by
Tobias Döring
Browse files
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Plain Diff
Performance and code structure fixed
parent
295ebae7
No related branches found
No related tags found
1 merge request
!2
Evo neuro
Changes
3
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3 changed files
main.py
+9
-7
9 additions, 7 deletions
main.py
population.py
+33
-36
33 additions, 36 deletions
population.py
walker.py
+6
-6
6 additions, 6 deletions
walker.py
with
48 additions
and
49 deletions
main.py
+
9
−
7
View file @
cff99083
...
...
@@ -18,19 +18,21 @@ if TEST_WALKER:
if
__name__
==
'
__main__
'
:
population
=
Population
(
POP_SIZE
,
H1
,
MUTATION_FACTOR
,
LOAD_BRAIN
,
VERSION
,
LEARNING_RATE
,
RENDER_BEST
)
avg_rewards
=
[]
if
TEST_WALKER
:
while
True
:
population
.
test_walker
()
while
GAME_CANCELLED
is
False
:
# this is our game
start_time
=
time
.
time
()
print
(
f
'
Gen:
{
population
.
gen
}
'
)
print
(
f
'
Max Steps:
{
population
.
max_steps
}
'
)
population
.
mutate
()
population
.
play_episode
()
population
.
evolve
()
#population.increase_moves(INCREASE_BY)
print
(
f
'
Gen:
{
population
.
gen
}
'
)
#print(f'Best Index: {population.best_walker_index}')
#print(f'Best Fitness: {population.fitnesses[population.best_walker_index]}')
print
(
f
'
Max Steps:
{
population
.
max_steps
}
'
)
# time.sleep(0.1)
#if population.gen % 10 == 0:
# population.walker.save()
print
(
"
Time for Gen:
"
,
time
.
time
()
-
start_time
)
if
population
.
gen
%
10
==
0
:
population
.
walker
.
save
()
avg_rewards
.
append
(
population
.
get_walker_stats
())
This diff is collapsed.
Click to expand it.
population.py
+
33
−
36
View file @
cff99083
...
...
@@ -7,7 +7,7 @@ import gym
np
.
random
.
seed
(
42
)
MAX_STEPS
=
1599
# after 1600 steps the Environment gives us a done anyway.
MAX_STEPS
=
300
# after 1600 steps the Environment gives us a done anyway.
class
Population
:
...
...
@@ -21,19 +21,15 @@ class Population:
self
.
max_steps
=
MAX_STEPS
self
.
render_best
=
render_best
self
.
env
=
gym
.
make
(
'
BipedalWalker-v3
'
)
self
.
weights
=
{}
self
.
weights
[
'
W1
'
]
=
np
.
random
.
randn
(
24
,
h1
)
/
np
.
sqrt
(
24
)
self
.
weights
[
'
W2
'
]
=
np
.
random
.
randn
(
h1
,
4
)
/
np
.
sqrt
(
h1
)
#self.walker = Walker(h1, version, load_brain, self.env, self.max_steps)
# maybe only have one mutatnt that is only used to test all the modifications/mutations
self
.
mutant
=
Walker
(
h1
,
version
,
load_brain
,
self
.
env
,
self
.
max_steps
)
self
.
walker
=
Walker
(
h1
,
version
,
load_brain
,
self
.
env
,
self
.
max_steps
)
self
.
mutated_weights
=
dict
()
#
self.mutants = []
self
.
mutants
=
[]
self
.
envs
=
[]
self
.
rewards
=
None
self
.
average_reward
=
None
self
.
lr
=
lr
#
for i in range(self.size):
#
self.mutants.append(Walker(h1, version, load_brain, self.env, self.max_steps))
for
i
in
range
(
self
.
size
):
self
.
mutants
.
append
(
Walker
(
h1
,
version
,
load_brain
,
self
.
env
,
self
.
max_steps
))
if
load_brain
:
self
.
mutate
()
...
...
@@ -69,41 +65,42 @@ class Population:
def
play_episode
(
self
):
self
.
rewards
=
np
.
zeros
(
self
.
size
)
mutated_weights
=
dict
()
for
i
in
range
(
self
.
size
):
for
k
,
v
in
self
.
weights
.
items
():
mutated_weights
[
k
]
=
v
+
self
.
mutation_factor
*
self
.
mutated_weights
[
k
][
i
]
self
.
rewards
[
i
]
=
self
.
get_reward
(
mutated_weights
)
#self.mutant.set_weights(mutated_weights)
#self.rewards[i] = self.mutant.get_reward()
self
.
rewards
[
i
]
=
self
.
mutants
[
i
].
get_reward
()
def
evolve
(
self
):
#R = self.rewards
A
=
(
self
.
rewards
-
np
.
mean
(
self
.
rewards
))
/
np
.
std
(
self
.
rewards
)
#weights = self.walker.get_weights()
#weights = self.weights
for
k
in
self
.
weights
:
weights_change
=
np
.
dot
(
self
.
mutated_weights
[
k
].
transpose
(
1
,
2
,
0
),
A
)
self
.
weights
[
k
]
=
self
.
weights
[
k
]
+
self
.
lr
/
(
self
.
size
*
self
.
mutation_factor
)
*
weights_change
#self.walker.set_weights(weights)
weights
=
self
.
walker
.
get_weights
()
for
i
in
range
(
self
.
size
):
for
k
in
weights
:
weights_change
=
np
.
dot
(
self
.
mutants
[
i
].
weights
[
k
].
T
,
A
[
i
]).
T
weights
[
k
]
=
weights
[
k
]
+
self
.
lr
/
(
self
.
size
*
self
.
mutation_factor
)
*
weights_change
self
.
walker
.
set_weights
(
weights
)
for
mutant
in
self
.
mutants
:
mutant
.
set_weights
(
weights
)
self
.
gen
+=
1
if
self
.
render_best
:
self
.
test_walker
()
self
.
test_walker
(
self
.
render_best
)
def
test_walker
(
self
,
render
):
reward
=
self
.
walker
.
get_reward
(
render
=
render
)
if
self
.
average_reward
is
None
:
self
.
average_reward
=
reward
else
:
self
.
average_reward
=
0.9
*
self
.
average_reward
+
0.1
*
reward
print
(
"
Current Reward:
"
,
reward
)
print
(
"
Average Reward:
"
,
self
.
average_reward
)
def
test_walker
(
self
):
#reward = self.walker.get_reward(render=True)
#print(reward)
return
def
get_walker_stats
(
self
):
avg_reward
=
self
.
walker
.
get_reward
(
steps
=
10000
)
for
i
in
range
(
9
):
reward
=
self
.
walker
.
get_reward
(
steps
=
10000
)
avg_reward
=
0.9
*
avg_reward
+
0.1
*
reward
return
avg_reward
def
mutate
(
self
):
# mutates all the weights of the mutants
#for i in range(len(self.mutants)):
# self.mutants[i].mutate(self.mutation_factor)
#weights = self.walker.get_weights()
self
.
mutated_weights
=
{}
#weights = self.weights
for
k
,
v
in
self
.
weights
.
items
():
self
.
mutated_weights
[
k
]
=
np
.
random
.
randn
(
self
.
size
,
v
.
shape
[
0
],
v
.
shape
[
1
])
for
i
in
range
(
len
(
self
.
mutants
)):
self
.
mutants
[
i
].
mutate
(
self
.
mutation_factor
)
# def increase_moves(self, size): # increase the number of directions for the brain
...
...
This diff is collapsed.
Click to expand it.
walker.py
+
6
−
6
View file @
cff99083
...
...
@@ -28,10 +28,12 @@ class Walker:
return
action
def
get_reward
(
self
,
render
=
False
):
def
get_reward
(
self
,
steps
=
None
,
render
=
False
):
observation
=
self
.
env
.
reset
()
total_reward
=
0
for
t
in
range
(
self
.
steps
):
if
steps
is
None
:
steps
=
self
.
steps
for
t
in
range
(
steps
):
if
render
:
self
.
env
.
render
()
action
=
self
.
get_action
(
observation
)
...
...
@@ -47,12 +49,10 @@ class Walker:
self
.
weights
[
k
]
=
v
+
mutation_rate
*
np
.
random
.
randn
(
v
.
shape
[
0
],
v
.
shape
[
1
])
def
get_weights
(
self
):
#return copy.deepcopy(self.weights)
return
self
.
weights
return
copy
.
deepcopy
(
self
.
weights
)
def
set_weights
(
self
,
weights
):
#self.weights = copy.deepcopy(weights)
self
.
weights
=
weights
self
.
weights
=
copy
.
deepcopy
(
weights
)
def
save
(
self
):
if
not
os
.
path
.
isdir
(
'
./models
'
):
...
...
This diff is collapsed.
Click to expand it.
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