lb11 = [0] * num_trials
val0 = [0] * num_trials
val1 = [0] * num_trials
for r in range(num_trials):
bidders[0].valuations = bidders[0].make_valuations()
learner.valuations = bidders[0].valuations
val0[r] = bidders[0].valuations[0]
if num_rounds > 1:
val1[r] = bidders[0].valuations[1]
if r == 0:
learner.calc_expected_rewards()
else:
learner.calc_terminal_state_rewards()
learner.solve_mdp()
b0[r] = bidders[0].place_bid(1)
lb0[r] = learner.place_bid(1)
if num_rounds > 1:
lb10[r] = learner.place_bid(2)
learner.num_goods_won = 1
lb11[r] = learner.place_bid(2)
learner.num_goods_won = 0
if not(learner.is_bidding_valuation_in_final_round()):
truthful_result_output = 'Does not bid truthfully in last round.'
else:
truthful_result_output = 'Truthful bidding in last round'
print(learner.valuations, round(b0[r], learner.digit_precision), lb0[r], lb10[r], lb11[r], truthful_result_output)
"""
plt.figure()
for s in learner.price_prediction.keys():
if len(s) == 2:
policies = {}
exp_payment = {}
print('See how learner bids')
calc_rewards = True
for v in itertools.product(possible_types, repeat=num_rounds):
learner.valuations = v
if calc_rewards:
learner.calc_expected_rewards()
calc_rewards = False
else:
learner.calc_terminal_state_rewards()
learner.solve_mdp()
policies[tuple(v)] = copy.deepcopy(learner.pi)
exp_payment[tuple(v)] = copy.deepcopy(learner.exp_payment)
print('Valuation vector =', v)
print('First round bid =', learner.place_bid(1))
for s in learner.state_space:
Q = {}
if s in learner.terminal_states:
continue
for a in learner.action_space:
Q[a] = learner.Q[(s, a)]
print('State', s, '. Optimal Action =', learner.pi[s], '. Q of each action:', Q)
print('Values at terminal states')
for s in learner.terminal_states:
print('State', s, '. V[s] =', learner.V[s])
if not (learner.is_bidding_valuation_in_final_round()):
truthful_result_output = 'Does not bid truthfully in last round.'
else:
truthful_result_output = 'Truthful bidding in last round'
print(truthful_result_output)
