RNaD reward transformation

[spktrm]

Based on formulae from the paper, the reward transformation is given by adding the log policy ratio

However, the code contains an entropy term instead.

https://github.com/deepmind/open_spiel/blob/db0f4a78b1fd0bee0263d46d62fb4d693897329e/open_spiel/python/algorithms/rnad/rnad.py#L422

Which one is it?

[lanctot]

@perolat, @bartdevylder: any ideas?

[bartdevylder]

Hi, Thanks for your question. The merged_log_policy term in the line you posted actually already contains the log policy ratio. It is defined here: https://github.com/deepmind/open_spiel/blob/db0f4a78b1fd0bee0263d46d62fb4d693897329e/open_spiel/python/algorithms/rnad/rnad.py#L801 taking into account the interpolation between the two regularization policies.

[spktrm]

Hi

Thank you for your reply. I understand this already. I want to understand why the merged_log_policy is multiplied by the policy in the code when this is not communicated in the paper.

[bartdevylder]

Hi, ok now I see your point. The eta_log_policy variable corresponds to the regularisation described in the paper, but the meaning of eta_reg_entropy is not so clear. @perolat will look into this to clarify

[spktrm]

@perolat any updates on this?

[sbl1996]

@spktrm Do you know the reason? Thanks.

[spktrm]

@spktrm Do you know the reason? Thanks.

Nope, unfortunately. Waiting for @perolat or related to clarify.