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jimkon committed Jan 19, 2018
1 parent edd59c7 commit 02f8e5c
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Showing 3 changed files with 20 additions and 17 deletions.
6 changes: 5 additions & 1 deletion src/main.py
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
AUTO_SAVE_AFTER_EPISODES = 500


def run(episodes=2500,
def run(episodes=250,
collecting_data=False,
experiment='InvertedPendulum-v1',
max_actions=1e3,
Expand Down Expand Up @@ -114,6 +114,10 @@ def run(episodes=2500,

data_fetcher.save()

# printing average times for running and training steps
data_fetcher.print_times(other_keys=data_fetcher.get_keys('run'))
data_fetcher.print_times(other_keys=data_fetcher.get_keys('agent_'), total_time_field='count')


if __name__ == '__main__':
run()
18 changes: 8 additions & 10 deletions src/show_results.py
Original file line number Diff line number Diff line change
Expand Up @@ -13,20 +13,18 @@ def show():
experiment = 'InvertedPendulum-v1'
v = 3

name = '{}data_{}_Wolp{}_{}k{}_{}'.format(folder,
episodes,
v,
actions,
k,
experiment
)
name = '{}data_{}_Wolp{}_{}k{}_{}_shrinked'.format(folder,
episodes,
v,
actions,
k,
experiment
)

fd = Agent_data(name)

fd.load()
# printing average times for running and training steps
# fd.print_times(other_keys=fd.get_keys('run'))
# fd.print_times(other_keys=fd.get_keys('agent_'), total_time_field='count')

plot_rewards(fd)

plot_average_reward(fd)
Expand Down
13 changes: 7 additions & 6 deletions src/util/agent_data.py
Original file line number Diff line number Diff line change
Expand Up @@ -283,12 +283,13 @@ def get_full_episode_data(self, ep):
def get_number_of_episodes(self):
return len(self.get_data('rewards'))

def get_adaption_episode(self, reward_threshold=50):
eps = self.get_episodes_with_reward_greater_than(reward_threshold)
if len(eps) > 0:
return eps[0]
else:
return -1
def get_adaption_episode(self, reward_threshold=10):
rewards = self.get_data('rewards')
total = 0
for i in range(len(rewards)):
total += rewards[i]
if total / (i + 1) > reward_threshold:
return i

def get_adaption_time(self, reward_threshold=50):
first_increase = self.get_adaption_episode(reward_threshold)
Expand Down

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