From b8e41dc1a6721009be8ba26f46f6bd99a38d519f Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Sun, 1 Sep 2019 09:19:11 -0400
Subject: [PATCH] using new level generator for training and inference
---
torch_training/dueling_double_dqn.py | 1 +
torch_training/render_agent_behavior.py | 57 +++++++++++++++++++------
torch_training/training_navigation.py | 48 ++++++++++++++++-----
3 files changed, 82 insertions(+), 24 deletions(-)
diff --git a/torch_training/dueling_double_dqn.py b/torch_training/dueling_double_dqn.py
index 4c2f0fa..dd67b4f 100644
--- a/torch_training/dueling_double_dqn.py
+++ b/torch_training/dueling_double_dqn.py
@@ -20,6 +20,7 @@ double_dqn = True # If using double dqn algorithm
input_channels = 5 # Number of Input channels
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
print(device)
diff --git a/torch_training/render_agent_behavior.py b/torch_training/render_agent_behavior.py
index 3882cc9..44c02ac 100644
--- a/torch_training/render_agent_behavior.py
+++ b/torch_training/render_agent_behavior.py
@@ -9,8 +9,8 @@ import torch_training.Nets
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnv
-from flatland.envs.rail_generators import complex_rail_generator
-from flatland.envs.schedule_generators import complex_schedule_generator
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.schedule_generators import sparse_schedule_generator
from flatland.utils.rendertools import RenderTool
from torch_training.dueling_double_dqn import Agent
from utils.observation_utils import norm_obs_clip, split_tree
@@ -27,20 +27,51 @@ x_dim = env.width
y_dim = env.height
"""
-x_dim = np.random.randint(8, 20)
-y_dim = np.random.randint(8, 20)
-n_agents = np.random.randint(3, 8)
-n_goals = n_agents + np.random.randint(0, 3)
-min_dist = int(0.75 * min(x_dim, y_dim))
+# Parameters for the Environment
+x_dim = 20
+y_dim = 20
+n_agents = 1
+n_goals = 5
+min_dist = 5
+
+# We are training an Agent using the Tree Observation with depth 2
+observation_builder = TreeObsForRailEnv(max_depth=2)
+
+# Use a the malfunction generator to break agents from time to time
+stochastic_data = {'prop_malfunction': 0.1, # Percentage of defective agents
+ 'malfunction_rate': 30, # Rate of malfunction occurence
+ 'min_duration': 3, # Minimal duration of malfunction
+ 'max_duration': 20 # Max duration of malfunction
+ }
+
+# Custom observation builder
+TreeObservation = TreeObsForRailEnv(max_depth=2)
+
+# Different agent types (trains) with different speeds.
+speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
env = RailEnv(width=x_dim,
height=y_dim,
- rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
- max_dist=99999,
- seed=0),
- schedule_generator=complex_schedule_generator(),
- obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
- number_of_agents=n_agents)
+ rail_generator=sparse_rail_generator(num_cities=5,
+ # Number of cities in map (where train stations are)
+ num_intersections=4,
+ # Number of intersections (no start / target)
+ num_trainstations=10, # Number of possible start/targets on map
+ min_node_dist=3, # Minimal distance of nodes
+ node_radius=2, # Proximity of stations to city center
+ num_neighb=3,
+ # Number of connections to other cities/intersections
+ seed=15, # Random seed
+ grid_mode=True,
+ enhance_intersection=False
+ ),
+ schedule_generator=sparse_schedule_generator(speed_ration_map),
+ number_of_agents=n_agents,
+ stochastic_data=stochastic_data, # Malfunction data generator
+ obs_builder_object=TreeObservation)
env.reset(True, True)
observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index fb03432..3110590 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -10,8 +10,8 @@ from dueling_double_dqn import Agent
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.rail_env import RailEnv
-from flatland.envs.rail_generators import complex_rail_generator
-from flatland.envs.schedule_generators import complex_schedule_generator
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.schedule_generators import sparse_schedule_generator
from flatland.utils.rendertools import RenderTool
from utils.observation_utils import norm_obs_clip, split_tree
@@ -30,8 +30,8 @@ def main(argv):
np.random.seed(1)
# Parameters for the Environment
- x_dim = 10
- y_dim = 10
+ x_dim = 20
+ y_dim = 20
n_agents = 1
n_goals = 5
min_dist = 5
@@ -39,15 +39,41 @@ def main(argv):
# We are training an Agent using the Tree Observation with depth 2
observation_builder = TreeObsForRailEnv(max_depth=2)
- # Load the Environment
+ # Use a the malfunction generator to break agents from time to time
+ stochastic_data = {'prop_malfunction': 0.1, # Percentage of defective agents
+ 'malfunction_rate': 30, # Rate of malfunction occurence
+ 'min_duration': 3, # Minimal duration of malfunction
+ 'max_duration': 20 # Max duration of malfunction
+ }
+
+ # Custom observation builder
+ TreeObservation = TreeObsForRailEnv(max_depth=2)
+
+ # Different agent types (trains) with different speeds.
+ speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
+
env = RailEnv(width=x_dim,
height=y_dim,
- rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
- max_dist=99999,
- seed=0),
- schedule_generator=complex_schedule_generator(),
- obs_builder_object=observation_builder,
- number_of_agents=n_agents)
+ rail_generator=sparse_rail_generator(num_cities=5,
+ # Number of cities in map (where train stations are)
+ num_intersections=4,
+ # Number of intersections (no start / target)
+ num_trainstations=10, # Number of possible start/targets on map
+ min_node_dist=3, # Minimal distance of nodes
+ node_radius=2, # Proximity of stations to city center
+ num_neighb=3,
+ # Number of connections to other cities/intersections
+ seed=15, # Random seed
+ grid_mode=True,
+ enhance_intersection=False
+ ),
+ schedule_generator=sparse_schedule_generator(speed_ration_map),
+ number_of_agents=n_agents,
+ stochastic_data=stochastic_data, # Malfunction data generator
+ obs_builder_object=TreeObservation)
env.reset(True, True)
# After training we want to render the results so we also load a renderer
--
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