Source codes for "Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics".
Requires numpy, scipy, and Pillow.
Note: Codes of deep reinforcement learning (DRL) algorithms stored in 'DRL_algorithms' require Pytorch, instead of scipy.
Most of hyperparameters are given in class 'hyperparameters'.
Whether parameter sharing is adopted or not is decided by Env.experience_sharing.
2024/03/22 modified inefficient calculations.
2024/03/22 uplaoded DRL codes used for comparison of performance.
[Added on 2025-07-31 to increase transparency.] The deep learning codes used for comparison were influenced by multiple open-source implementations, including:
- https://github.com/ikostrikov/pytorch-a3c (MIT license)
- https://github.com/nikhilbarhate99/PPO-PyTorch (MIT license)
In particular, the function memory_sample in pedestrian_task1_DQN.py and pedestrian_task2_DQN.py is based on the following implementation:
We sincerely appreciate the contributions of the original authors and acknowledge their impact on this work.