From 69cd0073519334f3d9c5b4e7ffb01a9d863416c9 Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Thu, 18 Jul 2019 12:59:28 -0400
Subject: [PATCH] enhanced functionality
---
scoring/score_test.py | 4 +-
scoring/utils/misc_utils.py | 2 +-
scoring/utils/observation_utils.py | 101 -----------------------------
3 files changed, 4 insertions(+), 103 deletions(-)
delete mode 100644 scoring/utils/observation_utils.py
diff --git a/scoring/score_test.py b/scoring/score_test.py
index 79f0ee6..5665d44 100644
--- a/scoring/score_test.py
+++ b/scoring/score_test.py
@@ -6,7 +6,8 @@ from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from torch_training.dueling_double_dqn import Agent
-from utils.misc_utils import run_test
+from scoring.utils.misc_utils import run_test
+from utils.observation_utils import normalize_observation
with open('parameters.txt', 'r') as inf:
parameters = eval(inf.read())
@@ -40,6 +41,7 @@ score_board = []
for test_nr in parameters:
current_parameters = parameters[test_nr]
test_score, test_dones, test_time = run_test(current_parameters, agent, observation_builder=observation_builder,
+ observation_wrapper=normalize_observation,
test_nr=test_nr, nr_trials_per_test=10)
print('{} score was {:.3f} with {:.2f}% environments solved. Test took {:.2f} Seconds to complete.\n'.format(
test_nr,
diff --git a/scoring/utils/misc_utils.py b/scoring/utils/misc_utils.py
index de30bad..b15476d 100644
--- a/scoring/utils/misc_utils.py
+++ b/scoring/utils/misc_utils.py
@@ -63,7 +63,7 @@ def run_test(parameters, agent, observation_builder=None, observation_wrapper=No
env = RailEnv(width=3,
height=3,
rail_generator=rail_from_file(file_name),
- obs_builder_object=observation_builder(),
+ obs_builder_object=observation_builder,
number_of_agents=1,
)
diff --git a/scoring/utils/observation_utils.py b/scoring/utils/observation_utils.py
deleted file mode 100644
index 787dfcf..0000000
--- a/scoring/utils/observation_utils.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import numpy as np
-
-
-def max_lt(seq, val):
- """
- Return greatest item in seq for which item < val applies.
- None is returned if seq was empty or all items in seq were >= val.
- """
- max = 0
- idx = len(seq) - 1
- while idx >= 0:
- if seq[idx] < val and seq[idx] >= 0 and seq[idx] > max:
- max = seq[idx]
- idx -= 1
- return max
-
-
-def min_lt(seq, val):
- """
- Return smallest item in seq for which item > val applies.
- None is returned if seq was empty or all items in seq were >= val.
- """
- min = np.inf
- idx = len(seq) - 1
- while idx >= 0:
- if seq[idx] >= val and seq[idx] < min:
- min = seq[idx]
- idx -= 1
- return min
-
-
-def norm_obs_clip(obs, clip_min=-1, clip_max=1, fixed_radius=0):
- """
- This function returns the difference between min and max value of an observation
- :param obs: Observation that should be normalized
- :param clip_min: min value where observation will be clipped
- :param clip_max: max value where observation will be clipped
- :return: returnes normalized and clipped observatoin
- """
- if fixed_radius > 0:
- max_obs = fixed_radius
- else:
- max_obs = max(1, max_lt(obs, 1000))
-
- min_obs = 0 #min(max_obs, min_lt(obs, 0))
-
- if max_obs == min_obs:
- return np.clip(np.array(obs) / max_obs, clip_min, clip_max)
- norm = np.abs(max_obs - min_obs)
- if norm == 0:
- norm = 1.
- return np.clip((np.array(obs) - min_obs) / norm, clip_min, clip_max)
-
-
-def split_tree(tree, num_features_per_node=9, current_depth=0):
- """
- Splits the tree observation into different sub groups that need the same normalization.
- This is necessary because the tree observation includes two different distance:
- 1. Distance from the agent --> This is measured in cells from current agent location
- 2. Distance to targer --> This is measured as distance from cell to agent target
- 3. Binary data --> Contains information about presence of object --> No normalization necessary
- Number 1. will depend on the depth and size of the tree search
- Number 2. will depend on the size of the map and thus the max distance on the map
- Number 3. Is independent of tree depth and map size and thus must be handled differently
- Therefore we split the tree into these two classes for better normalization.
- :param tree: Tree that needs to be split
- :param num_features_per_node: Features per node ATTENTION! this parameter is vital to correct splitting of the tree.
- :param current_depth: Keeping track of the current depth in the tree
- :return: Returns the three different groups of distance and binary values.
- """
-
- if len(tree) < num_features_per_node:
- return [], [], []
-
- depth = 0
- tmp = len(tree) / num_features_per_node - 1
- pow4 = 4
- while tmp > 0:
- tmp -= pow4
- depth += 1
- pow4 *= 4
- child_size = (len(tree) - num_features_per_node) // 4
- """
- Here we split the node features into the different classes of distances and binary values.
- Pay close attention to this part if you modify any of the features in the tree observation.
- """
- tree_data = tree[:6].tolist()
- distance_data = [tree[6]]
- agent_data = tree[7:num_features_per_node].tolist()
- # Split each child of the current node and continue to next depth level
- for children in range(4):
- child_tree = tree[(num_features_per_node + children * child_size):
- (num_features_per_node + (children + 1) * child_size)]
- tmp_tree_data, tmp_distance_data, tmp_agent_data = split_tree(child_tree,
- num_features_per_node,
- current_depth=current_depth + 1)
- if len(tmp_tree_data) > 0:
- tree_data.extend(tmp_tree_data)
- distance_data.extend(tmp_distance_data)
- agent_data.extend(tmp_agent_data)
- return tree_data, distance_data, agent_data
--
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