From 3d1219e32cef517e502790e556f160b7092c103c Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Wed, 3 Jul 2019 13:03:24 -0400
Subject: [PATCH] Commented functions for better understanding
---
utils/observation_utils.py | 25 +++++++++++++++++++------
1 file changed, 19 insertions(+), 6 deletions(-)
diff --git a/utils/observation_utils.py b/utils/observation_utils.py
index 75e6b0a..63adfff 100644
--- a/utils/observation_utils.py
+++ b/utils/observation_utils.py
@@ -50,12 +50,19 @@ def norm_obs_clip(obs, clip_min=-1, clip_max=1):
def split_tree(tree, num_features_per_node=8, current_depth=0):
"""
-
- :param tree:
- :param num_features_per_node:
- :param prompt:
- :param current_depth:
- :return:
+ 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:
@@ -69,9 +76,15 @@ def split_tree(tree, num_features_per_node=8, current_depth=0):
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[:4].tolist()
distance_data = [tree[4]]
agent_data = tree[5: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)]
--
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