def default_mcts_parameters():
parameters = MctsParameters()
parameters.DISCOUNT_FACTOR = 0.9
parameters.RANDOM_SEED = 1000
parameters.MAX_SEARCH_TIME = 1000
parameters.MAX_NUMBER_OF_ITERATIONS = 10000
parameters.MAX_SEARCH_DEPTH = 1000
parameters.MAX_NUMBER_OF_NODES = 10000
parameters.random_heuristic.MAX_SEARCH_TIME = 10
parameters.random_heuristic.MAX_NUMBER_OF_ITERATIONS = 1000
parameters.uct_statistic.LOWER_BOUND = -1000
parameters.uct_statistic.UPPER_BOUND = 100
parameters.uct_statistic.EXPLORATION_CONSTANT = 0.7
parameters.uct_statistic.PROGRESSIVE_WIDENING_ALPHA = 0.2
parameters.uct_statistic.PROGRESSIVE_WIDENING_K = 0.1
parameters.hypothesis_statistic.COST_BASED_ACTION_SELECTION = True
parameters.hypothesis_statistic.PROGRESSIVE_WIDENING_HYPOTHESIS_BASED = True
parameters.hypothesis_statistic.LOWER_COST_BOUND = 0
parameters.hypothesis_statistic.UPPER_COST_BOUND = 1
parameters.hypothesis_statistic.PROGRESSIVE_WIDENING_ALPHA = 0.5
parameters.hypothesis_statistic.PROGRESSIVE_WIDENING_K = 1
parameters.hypothesis_statistic.EXPLORATION_CONSTANT = 0.7
parameters.hypothesis_belief_tracker.RANDOM_SEED_HYPOTHESIS_SAMPLING = 1000
parameters.hypothesis_belief_tracker.HISTORY_LENGTH = 4
parameters.hypothesis_belief_tracker.PROBABILITY_DISCOUNT = 1.0
parameters.hypothesis_belief_tracker.POSTERIOR_TYPE = HypothesisBeliefTracker.PosteriorType.PRODUCT
return parameters
def test_pickle_unpickle(self):
from mamcts import MctsParameters, HypothesisBeliefTracker
from mamcts import CrossingStateDefaultParametersFloat, CrossingStateDefaultParametersInt
params_mcts = MctsParameters()
params_mcts.DISCOUNT_FACTOR = 0.9
params_mcts.RANDOM_SEED = 1000
params_mcts.MAX_SEARCH_TIME = 1232423
params_mcts.MAX_NUMBER_OF_ITERATIONS = 2315677
params_mcts.random_heuristic.MAX_SEARCH_TIME = 10
params_mcts.random_heuristic.MAX_NUMBER_OF_ITERATIONS = 1000
params_mcts.uct_statistic.LOWER_BOUND = -1000
params_mcts.uct_statistic.UPPER_BOUND = 100
params_mcts.uct_statistic.EXPLORATION_CONSTANT = 0.7
params_mcts.hypothesis_statistic.COST_BASED_ACTION_SELECTION = True
params_mcts.hypothesis_statistic.PROGRESSIVE_WIDENING_HYPOTHESIS_BASED = True
params_mcts.hypothesis_statistic.LOWER_COST_BOUND = 0
params_mcts.hypothesis_statistic.UPPER_COST_BOUND = 1
params_mcts.hypothesis_statistic.PROGRESSIVE_WIDENING_ALPHA = 0.5
params_mcts.hypothesis_statistic.PROGRESSIVE_WIDENING_K = 1
params_mcts.hypothesis_statistic.EXPLORATION_CONSTANT = 0.7
params_mcts.hypothesis_belief_tracker.RANDOM_SEED_HYPOTHESIS_SAMPLING = 1000
params_mcts.hypothesis_belief_tracker.HISTORY_LENGTH = 4
params_mcts.hypothesis_belief_tracker.PROBABILITY_DISCOUNT = 1.0
params_mcts.hypothesis_belief_tracker.POSTERIOR_TYPE = HypothesisBeliefTracker.PosteriorType.PRODUCT
params_mcts.hypothesis_belief_tracker.FIXED_HYPOTHESIS_SET = {
1: 5,
10: 4,
3: 100
}
params_mcts_unpickle = pu(params_mcts)
self.assertTrue(is_equal_mcts_params(params_mcts,
params_mcts_unpickle))
params_crossing_state = CrossingStateDefaultParametersFloat()
params_crossing_state_unpickle = pu(params_crossing_state)
self.assertTrue(
is_equal_crossing_state_params(params_crossing_state,
params_crossing_state_unpickle))
params_crossing_state = CrossingStateDefaultParametersInt()
params_crossing_state_unpickle = pu(params_crossing_state)
self.assertTrue(
is_equal_crossing_state_params(params_crossing_state,
params_crossing_state_unpickle))