def main_exp3(bidder, curr_rep, T, num_bidders, num_slots, outcome_space,
rank_scores, ctr, reserve, values, bids, threshold, noise):
algo_util = []
temp_regr = []
clean_alloc = [[] for _ in range(0, T)]
for t in range(0, T):
bid_chosen = bidder.bidding()
bids[t][0] = bid_chosen
bid_vec = deepcopy(bids[t])
gsp_instance = GSP(ctr[t], reserve[t], bid_vec, rank_scores[t],
num_slots, num_bidders)
allocated = gsp_instance.alloc_func(bidder.id, bids[t][bidder.id])
clean_alloc[t] = [
gsp_instance.alloc_func(bidder.id, bid * bidder.eps)
for bid in range(0, bidder.bid_space)
]
temp_alloc = deepcopy(clean_alloc[t])
noise_cp = deepcopy(noise)
bidder.alloc_func[t] = noise_mask(temp_alloc, noise_cp[t], ctr[t],
num_slots)
#reward function: value - payment(coming from GSP module)
bidder.pay_func[t] = [
gsp_instance.pay_func(bidder.id, bid * bidder.eps)
for bid in range(0, bidder.bid_space)
]
if allocated > threshold[t]:
bidder.reward_func[t] = [(values[t][0] - bidder.pay_func[t][b])
for b in range(0, bidder.bid_space)]
else:
bidder.reward_func[t] = [0 for _ in range(0, bidder.bid_space)]
bidder.utility[t] = normalize(bidder.reward_func[t], bidder.bid_space,
0, 1)
#weights update
arm_chosen = int(math.ceil(bids[t][0] / bidder.eps))
if bidder.pi[arm_chosen] < 0.0000000001:
bidder.pi[arm_chosen] = 0.0000000001
estimated_loss = bidder.utility[t][arm_chosen] / bidder.pi[arm_chosen]
bidder.loss[arm_chosen] += estimated_loss
arr = np.array([(-bidder.eta_exp3) * bidder.loss[b]
for b in range(0, bidder.bid_space)],
dtype=np.float128)
bidder.weights = np.exp(arr)
bidder.pi = [
bidder.weights[b] / sum(bidder.weights)
for b in range(0, bidder.bid_space)
]
#algo_util.append((bidder.reward_func[t][arm_chosen]*bidder.alloc_func[t][arm_chosen]))
algo_util.append(
(bidder.reward_func[t][arm_chosen] * clean_alloc[t][arm_chosen]))
temp_regr.append(
regret(bidder.reward_func, clean_alloc, bidder.bid_space,
algo_util, t))
return temp_regr
def main_exp3(bidder,curr_rep, T,num_bidders, num_slots, outcome_space, rank_scores, ctr, reserve, values,bids,threshold,noise,num_adaptive):
algo_util = []
temp_regr = []
clean_alloc = [[] for _ in range(0,T)]
for t in range(0,T):
bid_chosen = [bidder[i].bidding() for i in range(0,num_adaptive)]
for i in range(0,num_adaptive):
bids[t][i] = bid_chosen[i]
bid_vec = deepcopy(bids[t])
gsp_instance =GSP(ctr[t], reserve[t], bid_vec, rank_scores[t], num_slots, num_bidders)
arm_chosen =[0]*num_adaptive
for i in range(0,num_adaptive):
allocated = gsp_instance.alloc_func(bidder[i].id, bids[t][bidder[i].id])
temp = [gsp_instance.alloc_func(bidder[i].id, bid*bidder[i].eps) for bid in range(0, bidder[i].bid_space)]
if (i == 0):
clean_alloc[t] = deepcopy(temp)
noise_cp = deepcopy(noise)
bidder[i].alloc_func[t] = noise_mask(temp,noise_cp[t],ctr[t], num_slots)
#reward function: value - payment(coming from GSP module)
bidder[i].pay_func[t] = [gsp_instance.pay_func(bidder[i].id, bid*bidder[i].eps) for bid in range(0, bidder[i].bid_space)]
if (i == 0):
if allocated > threshold[t]:
bidder[i].reward_func[t] = [(values[t][i] - bidder[i].pay_func[t][b]) for b in range(0,bidder[i].bid_space)]
else:
bidder[i].reward_func[t] = [0 for _ in range(0,bidder[i].bid_space)]
bidder[i].utility[t] = bidder[i].reward_func[t]
#weights update
arm_chosen[i] = int(math.ceil(bids[t][i]/bidder[i].eps))
if bidder[i].pi[arm_chosen[i]] < 0.0000000001:
bidder[i].pi[arm_chosen[i]] = 0.0000000001
estimated_loss = -bidder[i].utility[t][arm_chosen[i]]/bidder[i].pi[arm_chosen[i]]
bidder[i].loss[arm_chosen[i]] += estimated_loss
arr = np.array([(-bidder[i].eta_exp3)*bidder[i].loss[b] for b in range(0,bidder[i].bid_space)], dtype=np.float128)
bidder[i].weights = np.exp(arr)
bidder[i].pi = [bidder[i].weights[b]/sum(bidder[i].weights) for b in range(0,bidder[i].bid_space)]
else:
if allocated > threshold[t]:
bidder[i].reward_func[t] = [(values[t][0] - bidder[i].pay_func[t][b]) for b in range(0,bidder[i].bid_space)]
bidder[i].utility[t] = bidder[i].compute_utility(1, bidder[i].reward_func[t], bidder[i].alloc_func[t])
else:
bidder[i].reward_func[t] = [0 for _ in range(0,bidder[i].bid_space)]
bidder[i].utility[t] = (bidder[i].compute_utility(0, bidder[i].reward_func[t], bidder[i].alloc_func[t]))
(bidder[i].weights, bidder[i].pi) = bidder[i].weights_update_winexp(bidder[i].eta_winexp, bidder[i].utility[t])
algo_util.append((bidder[0].reward_func[t][arm_chosen[0]]*clean_alloc[t][arm_chosen[0]]))
temp_regr.append(regret(bidder[0].reward_func,clean_alloc,bidder[0].bid_space, algo_util,t))
return temp_regr