Пример #1
0
 def __init__(self, bidder_id, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc):
     """
     :param bidder_id: Integer.  A unique identifier for this given agent.
     :param num_rounds: Integer.  The number of rounds the auction this bidder is participating in will run for.
     :param num_bidders: Integer.  The total number of bidders in the auction this bidder is participating in.
     :param possible_types: List.  A list of all possible types the bidder can take.  Types are arranged in
     increasing order.
     :param type_dist: List.  Probabilities corresponding to each entry in possible_types.
     :param type_dist_disc: Boolean.  True if type_dist is describing a discrete distribution.
     """
     # Init will call make_state_space, but we need some more information before going through this process.
     MDPBidderUAI.__init__(self, bidder_id, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc)
     self.num_price_samples = len(self.action_space)  # Not used
     self.num_prices_for_state = len(self.action_space) - 1
     state_price_delta = float(max(possible_types) - min(possible_types)) / self.num_prices_for_state
     self.prices_in_state = [i * state_price_delta for i in range(self.num_prices_for_state)]
     self.digit_precision = 2 # Learn values to this precision
     self.prices_in_state = set()
     self.state_space = set()
     self.terminal_states = set()
     self.action_space = set()
     self.use_given_pi = False
     self.given_pi = {}
if type_dist_disc:
    type_dist = [1.0 / len(possible_types)] * len(possible_types)
else:
    type_dist = [1.0] * len(possible_types)

num_mc = 50000

bidders = [KatzmanBidder(i, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc)
           for i in range(num_bidders)]
# bidders = [MenezesMonteiroBidder(i, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc)
#           for i in range(num_bidders)]
# bidders = [SimpleBidder(i, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc)
#           for i in range(num_bidders)]
# bidders = [WeberBidder(i, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc)
#           for i in range(num_bidders)]
learner = MDPBidderUAI(num_bidders, num_rounds, num_bidders, possible_types, type_dist, type_dist_disc)
learner.learn_auction_parameters(bidders, num_mc)

# Plot what the bidder has learned
learner.valuations = [.2, .1]
learner.calc_expected_rewards()
learner.solve_mdp()
plot_exp_payments(learner)
plot_transition(learner)
plot_prob_winning_and_transition(learner)
plot_Q_values(learner)
plot_price_pdf(learner)
print(learner.place_bid(1))
# print(learner.place_bid(2))

# Compare learner to other agents