From 21dba5dafe233949a4fa972f4626e9c6ab5fe31e Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Wed, 3 Jul 2019 12:56:39 -0400
Subject: [PATCH] added utils repo, moved normalization into utils
---
torch_training/training_navigation.py | 55 ++---------------
utils/__init__.py | 0
utils/observation_utils.py | 85 +++++++++++++++++++++++++++
3 files changed, 91 insertions(+), 49 deletions(-)
create mode 100644 utils/__init__.py
create mode 100644 utils/observation_utils.py
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index d085a8b..a6ee613 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -5,12 +5,13 @@ import matplotlib.pyplot as plt
import numpy as np
import torch
from dueling_double_dqn import Agent
-from flatland.envs.generators import complex_rail_generator
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.utils.rendertools import RenderTool
+from utils.observation_utils import norm_obs_clip, split_tree
+
random.seed(1)
np.random.seed(1)
@@ -39,7 +40,6 @@ env = RailEnv(width=15,
height=15,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=10, min_dist=10, max_dist=99999, seed=0),
number_of_agents=1)
-
"""
env = RailEnv(width=10,
height=20, obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv()))
@@ -54,6 +54,7 @@ env = RailEnv(width=20,
number_of_agents=15)
file_load = False
env.reset(True, True)
+
"""
env_renderer = RenderTool(env, gl="PILSVG",)
handle = env.get_agent_handles()
@@ -81,51 +82,7 @@ agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pt
demo = True
record_images = True
-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):
- """
- 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
- """
- max_obs = max(1, max_lt(obs, 1000))
- min_obs = 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)
+
for trials in range(1, n_trials + 1):
@@ -141,8 +98,8 @@ for trials in range(1, n_trials + 1):
final_obs_next = obs.copy()
for a in range(env.get_num_agents()):
print(a)
- data, distance, agent_data = env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=8,
- current_depth=0)
+ data, distance, agent_data = split_tree(tree=np.array(obs[a]), num_features_per_node=8,
+ current_depth=0)
data = norm_obs_clip(data)
distance = norm_obs_clip(distance)
agent_data = np.clip(agent_data, -1, 1)
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/utils/observation_utils.py b/utils/observation_utils.py
new file mode 100644
index 0000000..75e6b0a
--- /dev/null
+++ b/utils/observation_utils.py
@@ -0,0 +1,85 @@
+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):
+ """
+ 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
+ """
+ max_obs = max(1, max_lt(obs, 1000))
+ min_obs = 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=8, current_depth=0):
+ """
+
+ :param tree:
+ :param num_features_per_node:
+ :param prompt:
+ :param current_depth:
+ :return:
+ """
+
+ 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
+ tree_data = tree[:4].tolist()
+ distance_data = [tree[4]]
+ agent_data = tree[5:num_features_per_node].tolist()
+ 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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