large-scale-DRL-exploration

[RAL 2024] Deep Reinforcement Learning-based Large-scale Robot Exploration - - Public code and model

Run

Dependencies

• python == 3.10.8
• pytorch == 1.12.0
• ray == 2.1.0
• scikit-image == 0.19.3
• scikit-learn == 1.2.0
• scipy == 1.9.3
• matplotlib == 3.6.2
• tensorboard == 2.11.0

Training

1. Set training parameters in parameters.py.
2. Run python driver.py

Evaluation

1. Set parameters in test_parameters.py.
2. Run test_driver.py

Files

• parameters.py Training parameters.
• driver.py Driver of training program, maintain & update the global network.
• runner.py Wrapper of the local network.
• worker.py Interact with environment and collect episode experience.
• model.py Define attention-based network.
• env.py Autonomous exploration environment.
• graph_generator.py Generate and update the collision-free graph.
• ground_truth_graph.py Generate and update the ground truth graph.
• node.py Initialize and update nodes in the coliision-free graph.
• sensor.py Simulate the sensor model of Lidar.
• /model Trained model.
• /DungeonMaps Maps of training environments provided by Chen et al..

Authors

Yuhong Cao
Rui Zhao
Yizhuo Wang
Bairan Xiang
Guillaume Sartoretti