Jack Vice

I am getting same: `UndefVarError: py_gradients not defined #9 at /home/jack/.julia/packages/TensorFlow/q9pY2/src/TensorFlow.jl:189 ` With Julia 1.1 and Tensorflow 1.13.0

Almost, I kinda have 3 drones working in leader follower but I am seeing divergent behavior. I am confused by this code in the main loop of test_multiagent. I added...

I made this change in LeaderFollowerAviary.py line 84: rewards[0] = -1 * np.linalg.norm(np.array([0, .25, 0.5]) - states[0, 0:3])**2 and then ran gym-pybullet-drones/experiments/learning/multiagent.py I thought I tried setting ACT to ActionType.RPM....

Setting actiontype to rpm and "timesteps_total": 500000 and both drones crashed when I ran policy using the test_multiagent.py so I set "timesteps_total": 5000000 and got the following error running on...

I've tested on one machine that has ray 1.9.0 and torch version 1.10.1 and on another with ray 1.9.0 and torch 1.11.0 and policy still diverging . :( Thanks for...

To produce the error, I set `"timesteps_total": 10000000,` at line 283 of multiagent.py and then run `python multiagent.py --act 'rpm'`. The setup for the two machines I have tried are...

also tested with Nvidia Driver Version: 515.65.01 CUDA Version: 11.7 and the policy still diverges. is this a bug?

the default multi agent example from the paper does work including with an increase to "timesteps_total": 1200000. I did a fresh pull from the repo today and when I pass...

After training with `--act rpm`, I set line 315 to `action = {0: np.zeros(4), 1: np.zeros(4)}` and both drones appear to hover correctly.

Hi, the NaN divergence happens during training and running test_multiagent on the save dir fails with: `Traceback (most recent call last): File "test_multiagent.py", line 254, in with open(ARGS.exp+'/checkpoint.txt', 'r+') as...