Hiroyuki Vincent Yamazaki hvy

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              January 21, 2020 06:54
            
              
                A benchmark for https://github.com/optuna/optuna/pull/785
              
          
    The aim of this benchmark is to compare the performances of Optuna's pruners (i.e., NopPruner, MedianPruner, SuccessiveHalvingPruner and the ongoing HyperbandPruner). All of the pruners were used by the default settings in this benchmark.
The commands to execute this benchmark.
// (1) Downloads `kurobako` (BBO benchmark tool) binary.
$ curl -L https://github.com/sile/kurobako/releases/download/0.1.3/kurobako-0.1.3.linux-amd64 -o kurobako
$ chmod +x kurobako && sudo mv kurobako /usr/local/bin/

// (2) Downloads data files of HPOBench. (notice that the total size is over 700MB)
$ curl -OL http://ml4aad.org/wp-content/uploads/2019/01/fcnet_tabular_benchmarks.tar.gz

  
## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward