configuration module¶
Configuration for the simulations, for the single-player case.
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configuration.CPU_COUNT= 2¶ Number of CPU on the local machine
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configuration.HORIZON= 10000¶ HORIZON : number of time steps of the experiments. Warning Should be >= 10000 to be interesting “asymptotically”.
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configuration.DO_PARALLEL= True¶ To profile the code, turn down parallel computing
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configuration.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.REPETITIONS= 4¶ REPETITIONS : number of repetitions of the experiments. Warning: Should be >= 10 to be statistically trustworthy.
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configuration.RANDOM_SHUFFLE= False¶ The arms won’t be shuffled (
shuffle(arms)).
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configuration.RANDOM_INVERT= False¶ The arms won’t be inverted (
arms = arms[::-1]).
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configuration.NB_BREAK_POINTS= 0¶ Number of true breakpoints. They are uniformly spaced in time steps (and the first one at t=0 does not count).
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configuration.EPSILON= 0.1¶ Parameters for the epsilon-greedy and epsilon-… policies.
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configuration.TEMPERATURE= 0.05¶ Temperature for the Softmax policies.
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configuration.LEARNING_RATE= 0.01¶ Learning rate for my aggregated bandit (it can be autotuned)
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configuration.TEST_WrapRange= False¶ To know if my WrapRange policy is tested.
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configuration.CACHE_REWARDS= True¶ Should we cache rewards? The random rewards will be the same for all the REPETITIONS simulations for each algorithms.
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configuration.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.UNBIASED= False¶ 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.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.UNBOUNDED_VARIANCE= 1¶ Variance of unbounded Gaussian arms
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configuration.NB_ARMS= 9¶ Number of arms for non-hard-coded problems (Bayesian problems)
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configuration.LOWER= 0.0¶ Default value for the lower value of means
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configuration.AMPLITUDE= 1.0¶ Default value for the amplitude value of means
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configuration.VARIANCE= 0.05¶ Variance of Gaussian arms
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configuration.ARM_TYPE¶ alias of
Arms.Bernoulli.Bernoulli
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configuration.ENVIRONMENT_BAYESIAN= False¶ True to use bayesian problem
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configuration.MEANS= [0.05, 0.16249999999999998, 0.27499999999999997, 0.38749999999999996, 0.49999999999999994, 0.6125, 0.725, 0.8374999999999999, 0.95]¶ Means of arms for non-hard-coded problems (non Bayesian)
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configuration.USE_FULL_RESTART= True¶ True to use full-restart Doubling Trick
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configuration.configuration= {'append_labels': {}, 'cache_rewards': True, 'change_labels': {0: 'Pure exploration', 1: 'Pure exploitation', 2: '$\\varepsilon$-greedy', 3: 'Explore-then-Exploit', 5: 'Bernoulli kl-UCB', 6: 'Thompson sampling'}, '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]}], 'environment_bayesian': False, 'horizon': 10000, 'n_jobs': -1, 'nb_break_points': 0, 'plot_lowerbound': True, 'policies': [{'archtype': <class 'Policies.Uniform.Uniform'>, 'params': {}, 'change_label': 'Pure exploration'}, {'archtype': <class 'Policies.EmpiricalMeans.EmpiricalMeans'>, 'params': {}, 'change_label': 'Pure exploitation'}, {'archtype': <class 'Policies.EpsilonGreedy.EpsilonDecreasing'>, 'params': {'epsilon': 479.99999999999983}, 'change_label': '$\\varepsilon$-greedy'}, {'archtype': <class 'Policies.ExploreThenCommit.ETC_KnownGap'>, 'params': {'horizon': 10000, 'gap': 0.11250000000000004}, 'change_label': 'Explore-then-Exploit'}, {'archtype': <class 'Policies.UCBalpha.UCBalpha'>, 'params': {'alpha': 1}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'klucb': <function klucbBern>}, 'change_label': 'Bernoulli kl-UCB'}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'posterior': <class 'Policies.Posterior.Beta.Beta'>}, 'change_label': 'Thompson sampling'}], 'random_invert': False, 'random_shuffle': False, 'repetitions': 4, 'verbosity': 6}¶ This dictionary configures the experiments
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configuration.nbArms= 9¶ Number of arms in the first environment