Wireless Rechargable Sensors Network

Basic Hybrid Q_charging

Implementation of a Q_learning-based charging strategy on WRSN with multiple Mobile Chargers.

$ python Test.py
experiment_type:
experiment_index:

Experiment_index Experiment_type	0	1	2	3	4	5	6	7	8
node	`300`	`350`	400	`450`	`500`	550	600	650	700
target	`200`	`250`	300	`350`	`400`	450	500	550	600
MC	1	`2`	3	`4`	`5`	`6`	7	8	9
prob	0.1	0.2	0.3	`0.4`	`0.5`	0.6	`0.7`	`0.8`	0.9
package	400	450	500	550	`600`	`650`	700	`750`	`800`

target experiments must be reconstructed to match node experiments range if modified.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.vscode		.vscode
__pycache__		__pycache__
data		data
main		main
new_data		new_data
MobileCharger.py		MobileCharger.py
MobileCharger_Method.py		MobileCharger_Method.py
My_MobileCharger.py		My_MobileCharger.py
Network.py		Network.py
Network_Method.py		Network_Method.py
Node.py		Node.py
Node_Method.py		Node_Method.py
Package.py		Package.py
Parameter.py		Parameter.py
Q__learning.py		Q__learning.py
Q_learning_method.py		Q_learning_method.py
README.md		README.md
Test.py		Test.py
requirements.txt		requirements.txt