Deep Reinforced Landing

Node implementing the following functionalities:

Assigning a reward
Getting 'done' and 'environment' status
Get the image from the drone's camera
Publish velocity commands
Reset simulation for training a deep reinforcement learning (DRL) algorithm for making an unmanned aerial vehicle landing on a visual marker.

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.github/workflows		.github/workflows
README.md		README.md
boundingBox.cpp		boundingBox.cpp
clean_buffer_negative.py		clean_buffer_negative.py
clean_buffer_positive.py		clean_buffer_positive.py
count_experiences.py		count_experiences.py
debug_experience.py		debug_experience.py
drl_services_node.cpp		drl_services_node.cpp
drl_services_real_uav.cpp		drl_services_real_uav.cpp
experience_replay_buffer.py		experience_replay_buffer.py
extract_experiences.py		extract_experiences.py
merge_buffer.py		merge_buffer.py
multiappend_buffer.py		multiappend_buffer.py
multisplit_buffer.py		multisplit_buffer.py
package.xml		package.xml
q_network.py		q_network.py
remove_experiences.py		remove_experiences.py
replay_buffer.py		replay_buffer.py
rotate_replay_buffer.py		rotate_replay_buffer.py
setup.py		setup.py
shuffle_experiences.py		shuffle_experiences.py
split_buffer.py		split_buffer.py
teleop_keyboard.py		teleop_keyboard.py
teleop_spacenav.cpp		teleop_spacenav.cpp
test_buffer_architecture.py		test_buffer_architecture.py
utilities.cpp		utilities.cpp
visualise_action_distribution.py		visualise_action_distribution.py
visualise_action_distribution_real_UAV.py		visualise_action_distribution_real_UAV.py
visualise_experience.py		visualise_experience.py
visualize_action_distribution.py		visualize_action_distribution.py

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