from rlpy.Tools.hypersearch import find_hyperparameters best, trials = find_hyperparameters( "./experiment.py", "./Results/param_srch/", max_evals=10, parallelization="joblib", trials_per_point=5) print best
#!/usr/bin/env python from rlpy.Tools.hypersearch import find_hyperparameters best, trials = find_hyperparameters("./rbfs.py", "./RBFs_hypersearch", max_evals=10, parallelization="joblib", trials_per_point=5) print "Best parameters: ", best
from rlpy.Tools.hypersearch import find_hyperparameters best, trials = find_hyperparameters( "examples/tutorial/infTrackCartPole_rbfs.py", "./Results/Tutorial/InfTrackCartPole/RBFs_hypersearch", max_evals=10, parallelization="joblib", trials_per_point=5) print best
from __future__ import print_function from __future__ import unicode_literals from __future__ import division from __future__ import absolute_import from future import standard_library standard_library.install_aliases() from rlpy.Tools.hypersearch import find_hyperparameters best, trials = find_hyperparameters( "examples/tutorial/infTrackCartPole_rbfs.py", "./Results/Tutorial/InfTrackCartPole/RBFs_hypersearch", max_evals=10, parallelization="joblib", trials_per_point=5) print(best)
#!/usr/bin/env python from rlpy.Tools.hypersearch import find_hyperparameters best, trials = find_hyperparameters("./offline_simulator_1d_learner", "./RBFs_hypersearch", max_evals=10, parallelization="joblib", trials_per_point=5) print "Best parameters: ", best
from rlpy.Tools.hypersearch import find_hyperparameters best, trials = find_hyperparameters( "examples/gridworld/qlearning_hyperparamsearch.py", "./Results/gridworld/qlearning_hpsearch", max_evals=10, parallelization="joblib", trials_per_point=5) print best
from rlpy.Tools.hypersearch import find_hyperparameters import os import sys # cur_dir = os.path.expanduser("~/work/clipper/models/rl/") # sys.path.append(cur_dir) if __name__ == '__main__': experiment_name = sys.argv[1] best, trials = find_hyperparameters(experiment_name + "Experiment.py", "./Results/" + experiment_name + "/paramsearchnext", max_evals=10, parallelization="joblib", trials_per_point=6) print "============== This is the best parameters" print best