mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
a7206ed702
* Multiple improvements and bug fixes:
* Using lazy stacking to save on memory when using a replay buffer
* Remove step counting for evaluation episodes
* Reset game between heatup and training
* Major bug fixes in NEC (is reproducing the paper results for pong now)
* Image input rescaling to 0-1 is now optional
* Change the terminal title to be the experiment name
* Observation cropping for atari is now optional
* Added random number of noop actions for gym to match the dqn paper
* Fixed a bug where the evaluation episodes won't start with the max possible ale lives
* Added a script for plotting the results of an experiment over all the atari games
106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
import argparse
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import matplotlib
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import matplotlib.pyplot as plt
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from dashboard import SignalsFile
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import os
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class FigureMaker(object):
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def __init__(self, path, cols, smoothness, signal_to_plot, x_axis):
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self.experiments_path = path
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self.environments = self.list_environments()
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self.cols = cols
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self.rows = int((len(self.environments) + cols - 1) / cols)
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self.smoothness = smoothness
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self.signal_to_plot = signal_to_plot
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self.x_axis = x_axis
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params = {
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'axes.labelsize': 8,
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'font.size': 10,
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'legend.fontsize': 14,
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'xtick.labelsize': 8,
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'ytick.labelsize': 8,
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'text.usetex': False,
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'figure.figsize': [16, 30]
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}
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matplotlib.rcParams.update(params)
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def list_environments(self):
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environments = sorted([e.name for e in os.scandir(args.path) if e.is_dir()])
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filtered_environments = self.filter_environments(environments)
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return filtered_environments
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def filter_environments(self, environments):
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filtered_environments = []
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for idx, environment in enumerate(environments):
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path = os.path.join(args.path, environment)
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experiments = [e.name for e in os.scandir(path) if e.is_dir()]
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# take only the last updated experiment directory
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last_experiment_dir = max([os.path.join(path, root) for root in experiments], key=os.path.getctime)
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# make sure there is a csv file inside it
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for file_path in os.listdir(last_experiment_dir):
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full_file_path = os.path.join(last_experiment_dir, file_path)
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if os.path.isfile(full_file_path) and file_path.endswith('.csv'):
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filtered_environments.append((environment, full_file_path))
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return filtered_environments
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def plot_figures(self):
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for idx, (environment, full_file_path) in enumerate(self.environments):
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print(environment)
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axis = plt.subplot(self.rows, self.cols, idx + 1)
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signals = SignalsFile(full_file_path)
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signals.change_averaging_window(self.smoothness, force=True, signals=[self.signal_to_plot])
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steps = signals.bokeh_source.data[self.x_axis]
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rewards = signals.bokeh_source.data[self.signal_to_plot]
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yloc = plt.MaxNLocator(4)
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axis.yaxis.set_major_locator(yloc)
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axis.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
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plt.title(environment, fontsize=10, y=1.08)
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plt.plot(steps, rewards, linewidth=0.8)
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plt.subplots_adjust(hspace=2.0, wspace=0.4)
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def save_pdf(self, name):
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plt.savefig(name + ".pdf", bbox_inches='tight')
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def show_figures(self):
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plt.show()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-p', '--path',
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help="(string) Root directory of the experiments",
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default=None,
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type=str)
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parser.add_argument('-c', '--cols',
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help="(int) Number of plot columns",
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default=6,
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type=int)
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parser.add_argument('-s', '--smoothness',
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help="(int) Number of consequent episodes to average over",
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default=200,
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type=int)
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parser.add_argument('-sig', '--signal',
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help="(str) The name of the signal to plot",
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default='Evaluation Reward',
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type=str)
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parser.add_argument('-x', '--x_axis',
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help="(str) The meaning of the x axis",
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default='Total steps',
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type=str)
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parser.add_argument('-pdf', '--pdf',
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help="(str) A name of a pdf to save to",
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default='atari',
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type=str)
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args = parser.parse_args()
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maker = FigureMaker(args.path, cols=args.cols, smoothness=args.smoothness, signal_to_plot=args.signal, x_axis=args.x_axis)
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maker.plot_figures()
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maker.save_pdf(args.pdf)
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maker.show_figures()
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