bnelearn.tests.test_auction_learning module

Test auction learning in symmetric and asymmetric implementations, using a 2p-FPSB setup.

This script tests

  • whether the loop runs without runtime exceptions for a small number of iterations

  • whether the model learnt the appropriate bid for the top-range of valuations (this value is expected to be learned _very_ fast as it’s most significant and as such should always be found (up to a certain range) even in a short amount of time)

  • Further, the script tests whether the utility after 200 iterations is in the expected range, if it isn’t it won’t fail but issue a warning (because this might just be due to stochasticity as it would take a significantly longer test time / more iterations to make sure.)

bnelearn.tests.test_auction_learning.strat_to_bidder(strategy, batch_size, player_position=None)[source]
bnelearn.tests.test_auction_learning.test_learning_in_fpsb_environment()[source]

Tests the same setting as above (2p FPSB symmetric uniform), but with a fixed-environment implementation. (2 named agents with a shared model.)