configuration_comparing_aggregation_algorithms module¶
Configuration for the simulations, for the single-player case, for comparing Aggregation algorithms.
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configuration_comparing_aggregation_algorithms.HORIZON= 10000¶ HORIZON : number of time steps of the experiments. Warning Should be >= 10000 to be interesting “asymptotically”.
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configuration_comparing_aggregation_algorithms.REPETITIONS= 4¶ REPETITIONS : number of repetitions of the experiments. Warning: Should be >= 10 to be statistically trustworthy.
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configuration_comparing_aggregation_algorithms.DO_PARALLEL= True¶ To profile the code, turn down parallel computing
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configuration_comparing_aggregation_algorithms.N_JOBS= -1¶ Number of jobs to use for the parallel computations. -1 means all the CPU cores, 1 means no parallelization.
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configuration_comparing_aggregation_algorithms.NB_ARMS= 9¶ Number of arms for non-hard-coded problems (Bayesian problems)
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configuration_comparing_aggregation_algorithms.RANDOM_SHUFFLE= False¶ The arms are shuffled (
shuffle(arms)).
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configuration_comparing_aggregation_algorithms.RANDOM_INVERT= False¶ The arms are inverted (
arms = arms[::-1]).
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configuration_comparing_aggregation_algorithms.NB_RANDOM_EVENTS= 5¶ Number of random events. They are uniformly spaced in time steps.
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configuration_comparing_aggregation_algorithms.CACHE_REWARDS= False¶ Should we cache rewards? The random rewards will be the same for all the REPETITIONS simulations for each algorithms.
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configuration_comparing_aggregation_algorithms.UPDATE_ALL_CHILDREN= False¶ Should the Aggregator policy update the trusts in each child or just the one trusted for last decision?
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configuration_comparing_aggregation_algorithms.UNBIASED= True¶ Should the rewards for Aggregator policy use as biased estimator, ie just
r_t, or unbiased estimators,r_t / p_t
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configuration_comparing_aggregation_algorithms.UPDATE_LIKE_EXP4= False¶ Should we update the trusts proba like in Exp4 or like in my initial Aggregator proposal
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configuration_comparing_aggregation_algorithms.TRUNC= 1¶ Trunc parameter, ie amplitude, for Exponential arms
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configuration_comparing_aggregation_algorithms.VARIANCE= 0.05¶ Variance of Gaussian arms
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configuration_comparing_aggregation_algorithms.MINI= 0¶ lower bound on rewards from Gaussian arms
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configuration_comparing_aggregation_algorithms.MAXI= 1¶ upper bound on rewards from Gaussian arms, ie amplitude = 1
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configuration_comparing_aggregation_algorithms.SCALE= 1¶ Scale of Gamma arms
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configuration_comparing_aggregation_algorithms.ARM_TYPE¶ alias of
Arms.Bernoulli.Bernoulli
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configuration_comparing_aggregation_algorithms.configuration= {'cache_rewards': False, 'environment': [{'arm_type': <class 'Arms.Bernoulli.Bernoulli'>, 'params': [0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6, 0.7000000000000001, 0.8, 0.9]}], 'horizon': 10000, 'n_jobs': -1, 'nb_random_events': 5, 'policies': [{'archtype': <class 'Policies.Aggregator.Aggregator'>, 'params': {'children': [{'archtype': <class 'Policies.UCBalpha.UCBalpha'>, 'params': {'alpha': 1, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbBern>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbExp>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbGauss>}}, {'archtype': <class 'Policies.BayesUCB.BayesUCB'>, 'params': {'lower': 0, 'amplitude': 1}}], 'unbiased': True, 'update_all_children': False, 'decreaseRate': 'auto', 'update_like_exp4': False}}, {'archtype': <class 'Policies.Aggregator.Aggregator'>, 'params': {'children': [{'archtype': <class 'Policies.UCBalpha.UCBalpha'>, 'params': {'alpha': 1, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbBern>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbExp>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbGauss>}}, {'archtype': <class 'Policies.BayesUCB.BayesUCB'>, 'params': {'lower': 0, 'amplitude': 1}}], 'unbiased': True, 'update_all_children': False, 'decreaseRate': 'auto', 'update_like_exp4': True}}, {'archtype': <class 'Policies.LearnExp.LearnExp'>, 'params': {'children': [{'archtype': <class 'Policies.UCBalpha.UCBalpha'>, 'params': {'alpha': 1, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbBern>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbExp>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbGauss>}}, {'archtype': <class 'Policies.BayesUCB.BayesUCB'>, 'params': {'lower': 0, 'amplitude': 1}}], 'unbiased': True, 'eta': 0.9}}, {'archtype': <class 'Policies.UCBalpha.UCBalpha'>, 'params': {'alpha': 1, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbBern>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbExp>}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'lower': 0, 'amplitude': 1, 'klucb': <function klucbGauss>}}, {'archtype': <class 'Policies.BayesUCB.BayesUCB'>, 'params': {'lower': 0, 'amplitude': 1}}], 'random_invert': False, 'random_shuffle': False, 'repetitions': 4, 'verbosity': 6}¶ This dictionary configures the experiments
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configuration_comparing_aggregation_algorithms.LOWER= 0¶ And get LOWER, AMPLITUDE values
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configuration_comparing_aggregation_algorithms.AMPLITUDE= 1¶ And get LOWER, AMPLITUDE values