Open wsjeon opened 5 years ago
Also have a problem on action dimension when running codes follows README.md, with some private path problems...
@wsjeon Hi! May I ask if you eventually came up with weights with good performance (for expert trajectories with MACK)?
I am currently running the cooperative navigarion _"simple__spread" environment, and all losses rise at some point
@wsjeon @Ericonaldo An update for the code: I found new implementation of MAGAIL used for the new paper of ermongroup here. The paper is "Multi-Agent Adversarial Inverse Reinforcement Learning". Results of execution are similar to new paper, but I cannot reach the results of MAGAIL paper (based on the paper "Multi-Agent Generative Adversarial Imitation Learning").
Anyone reached performance from this paper with MAGAIL?
Dear authors,
Hi. Thank you for sharing your codes.
Recently, I've been interested in MAGAIL and tried to reproduce your results. Firstly, I tried to train expert policy as recommended in
README.md
bypython -m sandbox.mack.run_simple
, but I failed. I thought there is a problem on action dimension, so I made all actions as multi-hot vectors and modified relevant terms. After training with MACK, however, it seems like agents cannot recover the appropriate policies similar to MADDPG.So I wonder whether it is possible to share the weight files of expert so that readers can simply generate expert trajectories.
Thanks.