plt.style.use('ggplot') plt.switch_backend('agg') num_repetitions = 30 winexp = [] exp3 = [] min_num_rounds = 0 max_num_rounds = 5000 step = 5 num_adaptive = 4 rounds = [T for T in range(min_num_rounds, max_num_rounds)] #initialize the bidders once for the maximum number of rounds T = max_num_rounds (num_bidders, num_slots, outcome_space, rank_scores, ctr, reserve, values, threshold, noise) = set_auction_params(T, num_repetitions) # bids of the "adversaries" are considered fixed # bids size now: num_auctions x T x num_bidders bids = [] for t in range(0, T): bids.append([np.random.uniform(0, 1) for i in range(0, num_bidders)]) # Preferred Discretizations for the learner epsilon = 0.01 bidder_winexp = [[ Bidder(i, epsilon, T, outcome_space, num_repetitions) for i in range(0, num_adaptive) ] for _ in range(0, num_repetitions)] bidder_exp3 = [[ Bidder(i, epsilon, T, outcome_space, num_repetitions)
winexp = [] exp3 = [] min_num_rounds = 0 max_num_rounds = 5000 num_adaptive = 4 step = 1 rounds = [T for T in range(min_num_rounds,max_num_rounds, step)] matplotlib.rcParams.update({'font.size': 17}) fig = plt.figure() fig.set_figheight(10) fig.set_figwidth(10) plt.figure(1,figsize=(10,10)) #initialize the bidders once for the maximum number of rounds T = max_num_rounds (num_bidders, num_slots, outcome_space, rank_scores, ctr, reserve, values,threshold,noise) = set_auction_params(T,num_repetitions) # bids of the "adversaries" are considered fixed # bids size now: num_auctions x T x num_bidders bids = [] for t in range(0,T): bids.append([np.random.uniform(0,1) for i in range(0,num_bidders)]) eps_list = [0.001, 0.01, 0.1] for epsilon in eps_list: bidder_winexp = [[Bidder(i, epsilon, T, outcome_space, num_repetitions) for i in range(0,num_adaptive)] for _ in range(0,num_repetitions)] bidder_exp3 = [[Bidder(i, epsilon, T, outcome_space, num_repetitions) for i in range(0,num_adaptive)] for _ in range(0,num_repetitions)] # Preferred Discretizations for the learner cpy1 = deepcopy(bids)