diff --git a/parameters.txt b/parameters.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa83e8da75f0e84222a84cf1925f496b09f33112
--- /dev/null
+++ b/parameters.txt
@@ -0,0 +1,11 @@
+{'Test_0':[10,10,1,3],
+'Test_1':[10,10,3,4321],
+'Test_2':[10,10,5,123],
+'Test_3':[50,50,5,21],
+'Test_4':[50,50,20,85],
+'Test_5':[100,100,5,436],
+'Test_6':[100,100,20,6487],
+'Test_7':[100,100,50,567],
+'Test_8':[100,10,20,3245],
+'Test_9':[10,100,20,632]
+}
\ No newline at end of file
diff --git a/score_test.py b/score_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8bbda7ef257025c4dd79c303394f32cdef095a6
--- /dev/null
+++ b/score_test.py
@@ -0,0 +1,124 @@
+import random
+import time
+from collections import deque
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torch_training.dueling_double_dqn import Agent
+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 flatland.envs.generators import complex_rail_generator
+from utils.observation_utils import norm_obs_clip, split_tree
+from flatland.utils.rendertools import RenderTool
+from utils.misc_utils import printProgressBar, RandomAgent
+
+
+with open('parameters.txt','r') as inf:
+ parameters = eval(inf.read())
+
+# Parameter initialization
+features_per_node = 9
+state_size = features_per_node*21 * 2
+action_size = 5
+action_dict = dict()
+nr_trials_per_test = 100
+test_results = []
+test_times = []
+test_dones = []
+# Load agent
+#agent = Agent(state_size, action_size, "FC", 0)
+#agent.qnetwork_local.load_state_dict(torch.load('./torch_training/Nets/avoid_checkpoint30000.pth'))
+agent = RandomAgent(state_size, action_size)
+start_time_scoring = time.time()
+for test_nr in parameters:
+ current_parameters = parameters[test_nr]
+ print('\nRunning {} with (x_dim,ydim) = ({},{}) and {} Agents.'.format(test_nr,current_parameters[0],current_parameters[1],current_parameters[2]))
+ # Reset all measurements
+ time_obs = deque(maxlen=2)
+ test_scores = []
+
+ tot_dones = 0
+ tot_test_score = 0
+
+ # Reset environment
+ random.seed(current_parameters[3])
+ np.random.seed(current_parameters[3])
+ nr_paths = max(2,current_parameters[2] + int(0.5*current_parameters[2]))
+ min_dist = int(min([current_parameters[0], current_parameters[1]])*0.75)
+ env = RailEnv(width=current_parameters[0],
+ height=current_parameters[1],
+ rail_generator=complex_rail_generator(nr_start_goal=nr_paths, nr_extra=5, min_dist=min_dist, max_dist=99999,
+ seed=current_parameters[3]),
+ obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv()),
+ number_of_agents=current_parameters[2])
+ max_steps = max_steps = int(3 * (env.height + env.width))
+ agent_obs = [None] * env.get_num_agents()
+ env_renderer = RenderTool(env, gl="PILSVG", )
+ printProgressBar(0, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
+ start = time.time()
+ for trial in range(nr_trials_per_test):
+ # Reset the env
+ printProgressBar(trial+1, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
+ obs = env.reset(True, True)
+ #env_renderer.set_new_rail()
+ for a in range(env.get_num_agents()):
+ data, distance, agent_data = split_tree(tree=np.array(obs[a]), num_features_per_node=9,
+ current_depth=0)
+ data = norm_obs_clip(data)
+ distance = norm_obs_clip(distance)
+ agent_data = np.clip(agent_data, -1, 1)
+ obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
+
+ for i in range(2):
+ time_obs.append(obs)
+
+ for a in range(env.get_num_agents()):
+ agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
+
+ # Run episode
+ trial_score = 0
+ for step in range(max_steps):
+
+ for a in range(env.get_num_agents()):
+
+ action = agent.act(agent_obs[a], eps=0)
+ action_dict.update({a: action})
+
+ # Environment step
+ next_obs, all_rewards, done, _ = env.step(action_dict)
+
+ for a in range(env.get_num_agents()):
+ data, distance, agent_data = split_tree(tree=np.array(next_obs[a]), num_features_per_node=features_per_node,
+ current_depth=0)
+ data = norm_obs_clip(data)
+ distance = norm_obs_clip(distance)
+ agent_data = np.clip(agent_data, -1, 1)
+ next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
+ time_obs.append(next_obs)
+ for a in range(env.get_num_agents()):
+ agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
+ trial_score += all_rewards[a] / env.get_num_agents()
+ if done['__all__']:
+ tot_dones += 1
+ break
+ test_scores.append(trial_score / max_steps)
+ end = time.time()
+ comp_time = end-start
+ tot_test_score = np.mean(test_scores)
+ test_results.append(tot_test_score)
+ test_times.append(comp_time)
+ test_dones.append(tot_dones/nr_trials_per_test*100)
+end_time_scoring = time.time()
+tot_test_time = end_time_scoring-start_time_scoring
+test_idx = 0
+print('-----------------------------------------------')
+print(' RESULTS')
+print('-----------------------------------------------')
+for test_nr in parameters:
+ print('{} score was = {:.3f} with {:.2f}% environments solved. Test took {} Seconds to complete.'.format(test_nr,
+ test_results[test_idx],test_dones[test_idx],test_times[test_idx]))
+ test_idx += 1
+print('Total scoring duration was', tot_test_time)
\ No newline at end of file
diff --git a/torch_training/Nets/avoid_checkpoint15000.pth b/torch_training/Nets/avoid_checkpoint15000.pth
index a8e352a1b89046928ac6f5474cb08b3af792961c..1ab84e032c64b9b07e138bfa1191a7cfe09f9b6c 100644
Binary files a/torch_training/Nets/avoid_checkpoint15000.pth and b/torch_training/Nets/avoid_checkpoint15000.pth differ
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index dc01f7fadf21297087baa3ac0ac35513495a812a..65f1e9d00d6893732e96d3edbcab9642a07aeb2d 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -41,17 +41,18 @@ env = RailEnv(width=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()))
env.load("./railway/complex_scene.pkl")
file_load = True
"""
-env = RailEnv(width=10,
- height=10,
- rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=5, min_dist=10, max_dist=99999, seed=0),
+env = RailEnv(width=100,
+ height=100,
+ rail_generator=complex_rail_generator(nr_start_goal=100, nr_extra=5, min_dist=5, max_dist=99999, seed=0),
obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv()),
- number_of_agents=3)
+ number_of_agents=1)
file_load = False
env.reset(True, True)
"""
@@ -61,11 +62,11 @@ handle = env.get_agent_handles()
features_per_node = 9
state_size = features_per_node*21 * 2
action_size = 5
-n_trials = 15000
+n_trials = 30000
max_steps = int(3 * (env.height + env.width))
eps = 1.
eps_end = 0.005
-eps_decay = 0.9995
+eps_decay = 0.9997
action_dict = dict()
final_action_dict = dict()
scores_window = deque(maxlen=100)
@@ -77,9 +78,9 @@ action_prob = [0] * action_size
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
agent = Agent(state_size, action_size, "FC", 0)
-#agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pth'))
+agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint30000.pth'))
-demo = False
+demo = True
record_images = False
diff --git a/utils/misc_utils.py b/utils/misc_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..62189c52fda1a4f862df44268f5c815c1462a434
--- /dev/null
+++ b/utils/misc_utils.py
@@ -0,0 +1,51 @@
+# Print iterations progress
+import numpy as np
+def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '*'):
+ """
+ Call in a loop to create terminal progress bar
+ @params:
+ iteration - Required : current iteration (Int)
+ total - Required : total iterations (Int)
+ prefix - Optional : prefix string (Str)
+ suffix - Optional : suffix string (Str)
+ decimals - Optional : positive number of decimals in percent complete (Int)
+ length - Optional : character length of bar (Int)
+ fill - Optional : bar fill character (Str)
+ """
+ percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
+ filledLength = int(length * iteration // total)
+ bar = fill * filledLength + '_' * (length - filledLength)
+ print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=" ")
+ # Print New Line on Complete
+ if iteration == total:
+ print('')
+
+class RandomAgent:
+
+ def __init__(self, state_size, action_size):
+ self.state_size = state_size
+ self.action_size = action_size
+
+ def act(self, state, eps = 0):
+ """
+ :param state: input is the observation of the agent
+ :return: returns an action
+ """
+ return np.random.choice(np.arange(self.action_size))
+
+ def step(self, memories):
+ """
+ Step function to improve agent by adjusting policy given the observations
+
+ :param memories: SARS Tuple to be
+ :return:
+ """
+ return
+
+ def save(self, filename):
+ # Store the current policy
+ return
+
+ def load(self, filename):
+ # Load a policy
+ return