Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
D
DDPG
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Iterations
Requirements
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Locked files
Build
Pipelines
Jobs
Pipeline schedules
Test cases
Artifacts
Deploy
Releases
Package registry
Model registry
Operate
Environments
Terraform modules
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Code review analytics
Issue analytics
Insights
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Terms and privacy
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
TDDE19-2022-2
DDPG
Commits
cc717963
Commit
cc717963
authored
2 years ago
by
Marcus Gandal
Browse files
Options
Downloads
Patches
Plain Diff
Comment the code
parent
c723cb51
No related branches found
No related tags found
No related merge requests found
Changes
3
Hide whitespace changes
Inline
Side-by-side
Showing
3 changed files
src/agent.py
+12
-8
12 additions, 8 deletions
src/agent.py
src/networks.py
+3
-32
3 additions, 32 deletions
src/networks.py
src/run.py
+5
-1
5 additions, 1 deletion
src/run.py
with
20 additions
and
41 deletions
src/agent.py
+
12
−
8
View file @
cc717963
...
...
@@ -40,6 +40,7 @@ class Memory():
def
add
(
self
,
transition
):
"""
Add transition to memory buffer, if full, replace oldest transition
"""
self
.
memory
[
self
.
curr_index
%
self
.
max_capacity
]
=
transition
self
.
curr_index
=
self
.
curr_index
+
1
self
.
size
=
min
(
self
.
curr_index
,
self
.
max_capacity
)
...
...
@@ -215,6 +216,7 @@ class Agent():
def
get_velocity_state
(
self
):
"""
Get linear and angular velocies of the robot and return
"""
odom
=
rospy
.
wait_for_message
(
"
/odometry/filtered
"
,
Odometry
,
timeout
=
5
)
linear
=
odom
.
twist
.
twist
.
linear
.
x
angular
=
odom
.
twist
.
twist
.
angular
.
z
...
...
@@ -223,7 +225,7 @@ class Agent():
def
get_goal_state
(
self
):
"""
Get goal state
Currently a
ssumes flat ground (2D)
"""
A
ssumes flat ground (2D)
"""
position
,
orientation
=
self
.
get_position
()
diff_x
=
self
.
goal_x
-
position
.
x
diff_y
=
self
.
goal_y
-
position
.
y
...
...
@@ -275,20 +277,20 @@ class Agent():
self
.
has_arrived
=
True
print
(
"
GOAL REACHED!!!
"
)
#
Perform some scaling
#
Normalize values and set state
self
.
state
=
np
.
concatenate
((
laser_scan
/
self
.
max_distance
,
velocity
,
goal_state
/
np
.
array
([
self
.
max_goal_dist
,
np
.
pi
])))
# TODO: Add OU noise and clip to velocity intervals
def
choose_action
(
self
,
state
):
"""
Pick an action
"""
# Picks action from the actor, use during evaluation
tf_action
=
self
.
network
.
actor
(
tf
.
expand_dims
(
tf
.
convert_to_tensor
(
state
),
0
))
action
=
tf
.
squeeze
(
tf_action
).
numpy
()
#action[0] = (action[0] + np.random.uniform(0.0, 1.0)) * 0.25
#action[1] = (action[1] + np.random.uniform(-0.5, 0.5)) * 0.335
#action = np.array([np.random.uniform(0.0, 0.25), np.random.uniform(-0.5, 0.5)])
# Adds random noise for exploration, use during training
#action[0] = (action[0] + np.random.uniform(0.0, 1.0))
#action[1] = (action[1] + np.random.uniform(-0.5, 0.5))
return
action
*
np
.
array
([
0.35
,
0.35
])
...
...
@@ -309,6 +311,8 @@ class Agent():
def
train_step
(
self
):
"""
One update iteration of the networks
"""
batch
=
self
.
memory
.
sample
()
states
=
batch
[:,:
15
]
actions
=
batch
[:,
15
:
17
]
...
...
@@ -347,7 +351,7 @@ class Agent():
done
=
self
.
has_arrived
or
self
.
has_crashed
self
.
store_transition
(
state
,
action
,
reward
,
copy
.
deepcopy
(
self
.
state
),
done
)
# Update Networks
# Update Networks
, comment out during evaluation
#self.train_step()
return
reward
,
done
This diff is collapsed.
Click to expand it.
src/networks.py
+
3
−
32
View file @
cc717963
...
...
@@ -64,6 +64,7 @@ class ActorCritic():
return
model
# Update critic and actor networks
# Source: https://keras.io/examples/rl/ddpg_pendulum/
def
update_networks
(
self
,
states
,
actions
,
rewards
,
new_states
,
dones
):
...
...
@@ -94,6 +95,8 @@ class ActorCritic():
self
.
actor_optimizer
.
apply_gradients
(
zip
(
actor_grad
,
self
.
actor
.
trainable_variables
))
# Update target networks
# Source: https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/DDPG/tensorflow2/pendulum
def
update_target_networks
(
self
):
"""
Updates target actor and critic
"""
...
...
@@ -124,35 +127,3 @@ class ActorCritic():
self
.
target_actor
.
load_weights
(
TARGET_ACTOR_FILE_PATH
)
self
.
target_critic
.
load_weights
(
TARGET_CRITIC_FILE_PATH
)
print
(
"
Weights loaded.
"
)
if
__name__
==
"
__main__
"
:
# Test code
ac
=
ActorCritic
(
12
,
0.005
,
1
)
# Load weights
ac
.
load_weights_from_file
()
# Toy state
states
=
np
.
array
([[
0.0
,
1.0
,
2.0
,
3.0
,
4.0
,
5.0
,
6.0
,
7.0
,
8.0
,
9.0
,
10.0
,
11.0
],
[
12.0
,
13.0
,
14.0
,
15.0
,
16.0
,
17.0
,
18.0
,
19.0
,
20.0
,
21.0
,
22.0
,
23.0
],
[
24.0
,
25.0
,
26.0
,
27.0
,
28.0
,
29.0
,
30.0
,
31.0
,
32.0
,
33.0
,
34.0
,
35.0
]])
actions
=
np
.
array
([[
0.1
,
0.2
],
[
0.3
,
0.4
],
[
0.5
,
0.6
]])
# Forward propagate actor and critic networks
actions
=
ac
.
actor
(
states
)
y
=
ac
.
critic
([
states
,
actions
])
# Test target updates
ac
.
update_target_networks
()
# Save weights
ac
.
save_weights_to_file
()
print
(
y
)
print
(
"
PASS
"
)
This diff is collapsed.
Click to expand it.
src/run.py
+
5
−
1
View file @
cc717963
...
...
@@ -12,7 +12,7 @@ episode_rewards = []
"""
# Pre training
# Pre training
, comment out when finished
for i in range(1000):
agent.train_step()
print(
"
Step: {}
"
.format(i))
...
...
@@ -21,6 +21,7 @@ agent.save_weights()
"""
# Main program
for
episode
in
range
(
1
,
NUM_EPISODES
+
1
):
episode_reward
=
0.0
...
...
@@ -36,12 +37,15 @@ for episode in range(1, NUM_EPISODES + 1):
break
episode_rewards
.
append
(
episode_reward
)
# Moving average of past 40 episodes
avg_reward
=
np
.
mean
(
episode_rewards
[
-
min
(
40
,
episode
):])
print
(
"
End of episode:
"
,
episode
)
print
(
"
Average reward:
"
,
avg_reward
)
print
()
"""
# Comment out during evaluation
if episode % 3 == 0:
agent.memory.save_memory_to_file()
agent.save_weights()
...
...
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment