DADS reward implementation

[slee01]

Thank you for sharing your great code :)

I think I found that the reward function is a little different from what was defined in the paper(iclr2020):

dads/unsupervised_skill_learning/dads_agent.py

Lines 142 to 144 in abc37f5

	# final DADS reward
	intrinsic_reward = np.log(num_reps + 1) - np.log(1 + np.exp(
	np.clip(logp_altz - logp.reshape(1, -1), -50, 50)).sum(axis=0))

As far as I understand, the first reward term defined in eq. 6 of the paper is log q(s'|s,z) - log(\sum_{i=1}^{L}{q(s'|s,z_i)}). But the reward in this repo is defined as \sum_{i=1}^{L} {log q(s'|s,z) - log q(s'|s,z_i)} with numpy's broadcasting functionality. May I ask if I misunderstood or if there is any practical technique I'm missing?