UAV Obstacle Avoidance using Deep Recurrent Reinforcement Learning with Temporal Attention
The code is implemented in Tensorflow(version = 1.1.0) and Keras.
Requirements
The code is based on Python 2. Install dependency by running:
pip install --user -r requirements.txt
How to run
There are two types of DQN implementation with gpu: Keras and Tensorflow.
You can choose different implementation by altering line 15 in
main.py
Train original DQN:
python main.py --task_name 'DQN'
Train Double DQN:
python main.py --ddqn --task_name 'Double_DQN'
Train Dueling DQN:
python main.py --net_mode=duel --task_name 'Dueling_DQN'
Train Recurrent DQN:
python main.py --num_frames 10 --recurrent --task_name 'Recurrent_DQN'
Train Recurrent Temporal Attention DQN: (Using dqn_tf_temporalAt.py by uncommenting line 18 in main.py)
python main.py --num_frames 10 --recurrent --a_t --selector --task_name 'TemporalAt_DQN'
Train Recurrent Spatial Attention DQN: (Using dqn_tf_spatialAt.py by uncommenting line 21 in main.py)
python main.py --num_frames 10 --recurrent --a_t --selector --task_name 'SpatialAt_DQN'
Test trained model (e.g. Spatial Attention DQN):
python main.py --num_frames 10 --recurrent --a_t --selector --test \
--load_network --load_network_path=PATH_TO_NET
Acknowledgement
This code repository is highly inspired from work of Rui Zhu et al link.