ak-7 / imitation

Fork of Generative Adversarial Imitation Learning

Timing

(For Step 2, the major bottleneck)

• Classic: ~11 hours.
• ModernStochastic: ~60 hours. Gaaah.
• ModernEntreg: each regularization value takes ~10 hours, so ~30 hours total for the three values.
• Humanoid: N/A

Yeah, I wish there was a better way to do this sequentially. Probably Scala would be the best option. Remember that the classic ones and Reacher ("ModernEntreg") used seven random initializations.

Plots

Overall, these closely match the results in the GAIL paper.

Classical

Meh

Meh

Modern

Wow, HalfCheetah is very close to what's reported in the paper! The other two competing methods are barely above random, if at all. And the strong BC performance is corroborated by my rl_algorithms BC results. Interesting!

Yes, for some reason (and this is true in the GAIL paper) the two competing IL algorithms are really bad for Ant-v1, substantially worse than the expert. And my rl_algorithms code shows that indeed BC is good on Ant-v1.

Once again, this plot as a whole agrees with the GAIL paper. My rl_algorithms code agrees that BC doesn't do that well on Hopper-v1 until using lots of samples.

My rl_algorithms code agrees that BC doesn't do that well on Walker2d-v1, though these results are probably even worse! This figure is similar to what's in the GAIL paper, and yes GAIL seems to get superior performance compared to the expert.

Modern, Entropy Regularization

Wow, this is almost exactly like the figure in the GAIL paper!