14/05/2024: I use attention mechanism from SARL to re-rank human's observable vectors, which human have highest attention scores will feed into last cell of LSTM, the modification reduced average time to goal value from 12.38 to 11.44. Beside that, I test model which trained with circle crossing setting with square crossing setting (cross domain).
12/05/2024: Upload baseline with LSTM
Test result with 500 cases
Test case visualize
How to run
The same with original repository CrowdNavpython train.py --policy lstm_att
[2] Chen, Yu Fan, et al. "Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning." 2017 IEEE international conference on robotics and automation (ICRA). IEEE, 2017.
[3] Everett, Michael, Yu Fan Chen, and Jonathan P. How. "Motion planning among dynamic, decision-making agents with deep reinforcement learning." 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018.
[4] Chen, Changan, et al. "Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning." 2019 international conference on robotics and automation (ICRA). IEEE, 2019.
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