def get_all_graphs_infos(cls):
for (_, _, filenames) in os.walk(FOLDER_GRAPH):
for filename in filenames:
if '.gz' in filename:
graph_name = filename.replace('.gz',
'') # Remove .gz extension.
domain_name = filename.split('-', 1)[0]
options_list = [
'--domain', domain_name, '--graph', graph_name
]
exp_params = ExpParams.get_exp_params(options_list)
g = StateGraph.load(exp_params)
g.print_stats()
break
def probe_network(self):
exp_params = ExpParams.get_exp_params_from_command_line_args()
graph = exp_params.state_class.GAME_GRAPH
print "Network predictions:"
self.network_predictions = {} # Network predictions.
true_values = {
} # True values obtained from the graph using value iteration.
for state_roll_action_str in sorted(self.network_inputs.iterkeys()):
# state_value = self.network_outputs[state_str]
state_roll_action_value = self.network.activate(
self.network_inputs[state_roll_action_str])
self.network_predictions[
state_roll_action_str] = state_roll_action_value
node_id = graph.get_node_id(
state_roll_action_str[:-4]) # Removes roll and action.
true_value = graph.get_attr(node_id, VAL_ATTR)
true_values[state_roll_action_str] = true_value
# print "%s -> %s (%.2f)" % (state_str, state_value, abs_value)
for (si, ai), _ in sorted(self.visit_count.iteritems(),
key=lambda (k, v): (v, k)):
state_roll_action_str = '%s-%s' % (si, ai)
true_value = true_values[state_roll_action_str]
# Reward for white win is [1, 0],
# Reward for black win is [0, 1],
# state_value[0] - state_value[1] ranges from -1 to +1, although
# it can exceed those bounds when the network outputs are
# outside the range [0, 1].
# The following formula is meant to scale the difference to range [0, 1].
print "(%s, %s): opt. val. for white: %+.2f prediction: %s visited: %d" % (
si, ai, true_value,
map(PrettyFloat,
self.network_predictions[state_roll_action_str]),
self.visit_count.get((si, ai), 0))
print(
'Note: optimal values for white are based on the board '
'positions only and ignore the current roll.')
reverse=True)
for traj, cnt in sorted_traj_count:
print "%s: %d" % (traj, cnt)
# Reset after each query.
self.traj_count = {}
def print_learner_state(self):
self.print_visit_count()
self.print_e()
self.probe_network()
self.print_traj_counts()
if __name__ == '__main__':
make_data_folders()
exp_params = ExpParams.get_exp_params_from_command_line_args()
filename = exp_params.get_trial_filename(FILE_PREFIX_NTD)
f = open(filename, 'w')
agent_ntd = AgentNTD(exp_params.state_class)
if TRAIN_BUDDY == TRAIN_BUDDY_SELF:
agent_train_buddy = agent_ntd
elif TRAIN_BUDDY == TRAIN_BUDDY_COPY:
agent_train_buddy = AgentNTD(exp_params.state_class)
elif TRAIN_BUDDY == TRAIN_BUDDY_RANDOM:
agent_train_buddy = AgentRandom(exp_params.state_class)
agent_eval = Experiment.create_eval_opponent_agent(exp_params)
print 'Training buddy is: %s' % agent_train_buddy
print 'Evaluation opponent is: %s' % agent_eval
def get_knowledge_filename(cls):
exp_params = ExpParams.get_exp_params_from_command_line_args()
filename = exp_params.get_custom_filename_no_trial(FOLDER_QTABLE,
FILE_PREFIX_SARSA)
return filename