From 4eb2d5ffa10c0ec8ad53f3c25d86ada62cdc01cc Mon Sep 17 00:00:00 2001
From: "Egli Adrian (IT-SCI-API-PFI)" <adrian.egli@sbb.ch>
Date: Thu, 23 May 2019 09:25:12 +0200
Subject: [PATCH] demo.py added to show real-evn
---
env-data/railway/example_network_000.pkl | Bin 172 -> 180 bytes
env-data/railway/example_network_001.pkl | Bin 210 -> 218 bytes
env-data/railway/example_network_002.pkl | Bin 274 -> 282 bytes
examples/demo.py | 214 +++++++++++++++++++++++
flatland/envs/rail_env.py | 2 +
notebooks/Editor2.ipynb | 33 ++--
6 files changed, 237 insertions(+), 12 deletions(-)
create mode 100644 examples/demo.py
diff --git a/env-data/railway/example_network_000.pkl b/env-data/railway/example_network_000.pkl
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diff --git a/env-data/railway/example_network_001.pkl b/env-data/railway/example_network_001.pkl
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diff --git a/env-data/railway/example_network_002.pkl b/env-data/railway/example_network_002.pkl
index a598ca94bcd1193778ba1111be4371a14b3cae7c..898d54ebeb823e48790d4661ffe75a6940cd0712 100644
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diff --git a/examples/demo.py b/examples/demo.py
new file mode 100644
index 0000000..8f1638e
--- /dev/null
+++ b/examples/demo.py
@@ -0,0 +1,214 @@
+import os
+import random
+from collections import deque
+
+import numpy as np
+import torch
+
+from flatland.baselines.dueling_double_dqn import Agent
+from flatland.envs.generators import complex_rail_generator
+# from flatland.envs.generators import rail_from_list_of_saved_GridTransitionMap_generator
+from flatland.envs.generators import random_rail_generator
+from flatland.envs.rail_env import RailEnv
+from flatland.utils.rendertools import RenderTool
+
+# ensure that every demo run behave constantly equal
+random.seed(1)
+np.random.seed(1)
+
+
+class Scenario_Generator:
+ @staticmethod
+ def generate_random_scenario(number_of_agents=3):
+ # Example generate a rail given a manual specification,
+ # a map of tuples (cell_type, rotation)
+ transition_probability = [15, # empty cell - Case 0
+ 5, # Case 1 - straight
+ 5, # Case 2 - simple switch
+ 1, # Case 3 - diamond crossing
+ 1, # Case 4 - single slip
+ 1, # Case 5 - double slip
+ 1, # Case 6 - symmetrical
+ 0, # Case 7 - dead end
+ 1, # Case 1b (8) - simple turn right
+ 1, # Case 1c (9) - simple turn left
+ 1] # Case 2b (10) - simple switch mirrored
+
+ # Example generate a random rail
+
+ env = RailEnv(width=20,
+ height=20,
+ rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
+ number_of_agents=number_of_agents)
+
+ return env
+
+ @staticmethod
+ def generate_complex_scenario(number_of_agents=3):
+ env = RailEnv(width=15,
+ height=15,
+ rail_generator=complex_rail_generator(nr_start_goal=6, nr_extra=30, min_dist=10, max_dist=99999, seed=0),
+ number_of_agents=number_of_agents)
+
+ return env
+
+ @staticmethod
+ def load_scenario(filename, number_of_agents=3):
+ env = RailEnv(width=2 * (1 + number_of_agents),
+ height=1 + number_of_agents)
+
+ """
+ env = RailEnv(width=20,
+ height=20,
+ rail_generator=rail_from_list_of_saved_GridTransitionMap_generator(
+ [filename]),
+ number_of_agents=number_of_agents)
+ """
+ if os.path.exists(filename):
+ print("load file: ", filename)
+ env.load(filename)
+ env.reset(False, False)
+ else:
+ print("File does not exist:", filename, " Working directory: ", os.getcwd())
+
+ return env
+
+
+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 = max(0, 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)
+
+
+class Demo:
+
+ def __init__(self, env):
+ self.env = env
+ self.create_renderer()
+ self.load_agent()
+
+ def load_agent(self):
+ self.state_size = 105 * 2
+ self.action_size = 4
+ self.agent = Agent(self.state_size, self.action_size, "FC", 0)
+ self.agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint15000.pth'))
+
+ def create_renderer(self):
+ self.renderer = RenderTool(self.env, gl="QT")
+ handle = self.env.get_agent_handles()
+ return handle
+
+ def run_demo(self, max_nbr_of_steps=100):
+ action_dict = dict()
+ time_obs = deque(maxlen=2)
+ action_prob = [0] * 4
+ agent_obs = [None] * self.env.get_num_agents()
+ agent_next_obs = [None] * self.env.get_num_agents()
+
+ # Reset environment
+ obs = self.env.reset(False, False)
+
+ for a in range(self.env.get_num_agents()):
+ data, distance = self.env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=5, current_depth=0)
+
+ data = norm_obs_clip(data)
+ distance = norm_obs_clip(distance)
+ obs[a] = np.concatenate((data, distance))
+
+ for i in range(2):
+ time_obs.append(obs)
+
+ # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
+ for a in range(self.env.get_num_agents()):
+ agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
+
+ for step in range(max_nbr_of_steps):
+ self.renderer.renderEnv(show=True)
+
+ # print(step)
+ # Action
+ for a in range(self.env.get_num_agents()):
+ action = self.agent.act(agent_obs[a])
+ action_prob[action] += 1
+ action_dict.update({a: action})
+
+ # Environment step
+ next_obs, all_rewards, done, _ = self.env.step(action_dict)
+ for a in range(self.env.get_num_agents()):
+ data, distance = self.env.obs_builder.split_tree(tree=np.array(next_obs[a]), num_features_per_node=5,
+ current_depth=0)
+ data = norm_obs_clip(data)
+ distance = norm_obs_clip(distance)
+ next_obs[a] = np.concatenate((data, distance))
+
+ # Update replay buffer and train agent
+ for a in range(self.env.get_num_agents()):
+ agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
+
+ time_obs.append(next_obs)
+
+ agent_obs = agent_next_obs.copy()
+ if done['__all__']:
+ break
+
+
+if False:
+ demo_000 = Demo(Scenario_Generator.generate_random_scenario())
+ demo_000.run_demo()
+ demo_000 = None
+
+ demo_001 = Demo(Scenario_Generator.generate_complex_scenario())
+ demo_001.run_demo()
+ demo_001 = None
+
+demo_001 = Demo(Scenario_Generator.load_scenario('../env-data/railway/example_network_001.pkl'))
+demo_001.run_demo()
+demo_001 = None
+
+demo_002 = Demo(Scenario_Generator.load_scenario('../env-data/railway/example_network_002.pkl'))
+demo_002.run_demo()
+demo_002 = None
+
+demo_003 = Demo(Scenario_Generator.load_scenario('../env-data/railway/example_network_003.pkl'))
+demo_003.run_demo()
+demo_003 = None
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 118ebf4..74e7526 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -353,6 +353,8 @@ class RailEnv(Environment):
self.agents = [EnvAgent(d[0], d[1], d[2], d[3], d[4]) for d in data[b"agents"]]
# setup with loaded data
self.height, self.width = self.rail.grid.shape
+ self.rail.height = self.height
+ self.rail.width = self.width
# self.agents = [None] * self.get_num_agents()
self.dones = dict.fromkeys(list(range(self.get_num_agents())) + ["__all__"], False)
diff --git a/notebooks/Editor2.ipynb b/notebooks/Editor2.ipynb
index 4e9a3d7..5dcfd55 100644
--- a/notebooks/Editor2.ipynb
+++ b/notebooks/Editor2.ipynb
@@ -9,9 +9,18 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 25,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
@@ -19,7 +28,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -32,7 +41,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -54,7 +63,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -63,7 +72,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -97,7 +106,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 30,
"metadata": {
"scrolled": false
},
@@ -105,7 +114,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7c89d2a7999f41e0b2ee1f79b4fa3df0",
+ "model_id": "47af532101994c36a053e16a9b31dcd6",
"version_major": 2,
"version_minor": 0
},
@@ -123,7 +132,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 31,
"metadata": {
"scrolled": false
},
@@ -131,7 +140,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2d0119cf2c704437bec328b1d19dd741",
+ "model_id": "949dc7440647445e82dd1ca0f250e5ca",
"version_major": 2,
"version_minor": 0
},
@@ -150,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -159,7 +168,7 @@
"(0, 0)"
]
},
- "execution_count": 8,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
--
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