"""This module tests whether rewards can be calculated correctly in an environment."""
import torch
from bnelearn.strategy import TruthfulStrategy
from bnelearn.mechanism import FirstPriceSealedBidAuction
from bnelearn.bidder import Bidder
from bnelearn.environment import AuctionEnvironment
from bnelearn.sampler import UniformSymmetricIPVSampler
# Shared objects
cuda = torch.cuda.is_available()
device = 'cuda' if cuda else 'cpu'
n_players = 3
batch_size = 2**5
observation_size = valuation_size = action_size = 1
u_lo = 0
u_hi = 10
[docs]def strat_to_bidder(strategy, batch_size, player_position=0):
"""creates a bidder from a strategy"""
return Bidder(strategy, player_position, batch_size)
[docs]def test_auction_environment():
sampler = UniformSymmetricIPVSampler(
u_lo, u_hi, n_players, valuation_size, batch_size, device
)
bidders = [
strat_to_bidder(TruthfulStrategy(), batch_size, i)
for i in range(n_players)]
env = AuctionEnvironment(
FirstPriceSealedBidAuction(cuda), bidders, sampler, batch_size, n_players, strat_to_bidder
)
reward_0 = env.get_reward(bidders[0])
assert 1==1