DDPG Acting Deterministically

[JohnBurden]

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Hi,

I've been using the DDPG implementation on the pyBullet implementation of RaceCar (https://github.com/bulletphysics/bullet3/blob/master/examples/pybullet/gym/pybullet_envs/bullet/racecar.py) after wrapping for compatibility with this repo. When running some basic experiments (essentially the same as those in the Results directory) I noticed that DDPG appears to be acting deterministically. Every run is the same when I set config to handle multiple runs. I do not believe this should be the case as the environment itself has an element of stochasticity in how it generates goals, and also I think the DDPG learning process is stochastic also. Is this possibly indicative of some random number seeding issue within this repo or how multiple runs are being handled?

Thanks, John

[p-christ]

Hi,

Ah yes, at the moment if you choose the multiple runs option it will do multiple runs with the same random seed that you set. So that might be explaining what's happening - do you see different results if you change the random seed?

Thanks,

Petros