Comments (2)
@pender - Sorry for the slow reply, I need to change my settings so I receive notifications.
I'll look at your solution today. Even within the MuJoCo tasks there was inconsistency with how observations were returned. I think I even ran into mixed types within an observation. As you can see, I had to do lots of brute force casting.
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I think your solution was good, but I decided to use np.squeeze to remove the extra dimension. I'll push this into the master branch. I'm going to keep the aigym_evaluation branch frozen where it was when I ran all 10 MuJoCo environments. (Although the fix doesn't seem to cause a problem with the MuJoCo environments, they were just forgiving of having an extra dimension).
I'm glad you posted, I'm looking forward to trying the roboschool environments. I'm curious how the simulation speed compares.
OpenAI just posted a short PPO paper and they use a different loss function. I'll probably give that a try soon.
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