Implementation of paper Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning by Yu Fan Chen, Miao Liu, Michael Everett and Jonathan P. How
This library is no longer maintained. CADRL and GA3C-CADRL is also implemented in our newest library CrowdNav, which is easier to use and can be better used for benchmarking RL navigation algorithms.
Training with specified configuration on CPU:
python train.py --config=configs/model.config
Training with specified configuration on GPU:
python train.py --config=configs/model.config --gpu
Visualize the trained agent:
python visualize.py --output_dir={OUTPUT_DIR}
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All the training data for imitation learning/initialization of the model is generated with RVO2. Both evaluation and test are performed on the crossing scenarios.
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The kinematics can be toggled in the env.config, which gives a hard rotation constraint.