diff --git a/torch_training/bla.py b/torch_training/bla.py
deleted file mode 100644
index 584e50287ab6d59119b936cce7266f05c586c613..0000000000000000000000000000000000000000
--- a/torch_training/bla.py
+++ /dev/null
@@ -1,229 +0,0 @@
-import getopt
-import random
-import sys
-from collections import deque
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from importlib_resources import path
-
-import torch_training.Nets
-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 torch_training.dueling_double_dqn import Agent
-from utils.observation_utils import norm_obs_clip, split_tree
-
-print("multi_agent_trainging.py (1)")
-
-def main(argv):
- try:
- opts, args = getopt.getopt(argv, "n:", ["n_trials="])
- except getopt.GetoptError:
- print('training_navigation.py -n <n_trials>')
- sys.exit(2)
- for opt, arg in opts:
- if opt in ('-n', '--n_trials'):
- n_trials = int(arg)
- print("main1")
- random.seed(1)
- np.random.seed(1)
-
- """
- env = RailEnv(width=10,
- height=20, obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()))
- env.load("./railway/complex_scene.pkl")
- file_load = True
- """
-
- 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))
- print("main2")
-
- 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),
- obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
- number_of_agents=n_agents)
- env.reset(True, True)
- file_load = False
- observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
- env_renderer = RenderTool(env, gl="PILSVG", )
- handle = env.get_agent_handles()
- features_per_node = 9
- state_size = features_per_node * 85 * 2
- action_size = 5
-
- print("main3")
-
- # We set the number of episodes we would like to train on
- if 'n_trials' not in locals():
- n_trials = 30000
- max_steps = int(3 * (env.height + env.width))
- eps = 1.
- eps_end = 0.005
- eps_decay = 0.9995
- action_dict = dict()
- final_action_dict = dict()
- scores_window = deque(maxlen=100)
- done_window = deque(maxlen=100)
- time_obs = deque(maxlen=2)
- scores = []
- dones_list = []
- 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)
- with path(torch_training.Nets, "avoid_checkpoint30000.pth") as file_in:
- agent.qnetwork_local.load_state_dict(torch.load(file_in))
-
- demo = False
- record_images = False
- frame_step = 0
-
- print("Going to run training for {} trials...".format(n_trials))
- for trials in range(1, n_trials + 1):
-
- if trials % 50 == 0 and not demo:
- 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))
- 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),
- obs_builder_object=TreeObsForRailEnv(max_depth=3,
- predictor=ShortestPathPredictorForRailEnv()),
- number_of_agents=n_agents)
- env.reset(True, True)
- max_steps = int(3 * (env.height + env.width))
- agent_obs = [None] * env.get_num_agents()
- agent_next_obs = [None] * env.get_num_agents()
- # Reset environment
- if file_load:
- obs = env.reset(False, False)
- else:
- obs = env.reset(True, True)
- if demo:
- env_renderer.set_new_rail()
- obs_original = obs.copy()
- final_obs = obs.copy()
- final_obs_next = obs.copy()
- for a in range(env.get_num_agents()):
- data, distance, agent_data = split_tree(tree=np.array(obs[a]),
- 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))
- agent_data = env.agents[a]
- speed = 1 # np.random.randint(1,5)
- agent_data.speed_data['speed'] = 1. / speed
-
- 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(env.get_num_agents()):
- agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
-
- score = 0
- env_done = 0
- # Run episode
- for step in range(max_steps):
- if demo:
- env_renderer.renderEnv(show=True, show_observations=False)
- # observation_helper.util_print_obs_subtree(obs_original[0])
- if record_images:
- env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
- frame_step += 1
- # print(step)
- # Action
- for a in range(env.get_num_agents()):
- if demo:
- eps = 0
- # action = agent.act(np.array(obs[a]), eps=eps)
- print("before act")
- #action = agent.act(agent_obs[a], eps=eps)
- action = 0
- print("after act")
- action_prob[action] += 1
- action_dict.update({a: action})
- # Environment step
-
- next_obs, all_rewards, done, _ = env.step(action_dict)
- # print(all_rewards,action)
- obs_original = next_obs.copy()
- for a in range(env.get_num_agents()):
- a = 5
- # data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
- # 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)
- #
- # # Update replay buffer and train agent
- # for a in range(env.get_num_agents()):
- # agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
- # if done[a]:
- # final_obs[a] = agent_obs[a].copy()
- # final_obs_next[a] = agent_next_obs[a].copy()
- # final_action_dict.update({a: action_dict[a]})
- # if not demo and not done[a]:
- # agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a])
- # score += all_rewards[a] / env.get_num_agents()
- #
- # agent_obs = agent_next_obs.copy()
- # if done['__all__']:
- # env_done = 1
- # for a in range(env.get_num_agents()):
- # agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a])
- # break
- # # Epsilon decay
- # eps = max(eps_end, eps_decay * eps) # decrease epsilon
- #
- # done_window.append(env_done)
- # scores_window.append(score / max_steps) # save most recent score
- # scores.append(np.mean(scores_window))
- # dones_list.append((np.mean(done_window)))
-
- print(
- '\rTraining {} Agents on ({},{}).\t Episode {}\t Average Score: {:.3f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
- env.get_num_agents(), x_dim, y_dim,
- trials,
- np.mean(scores_window),
- 100 * np.mean(done_window),
- eps, action_prob / np.sum(action_prob)), end=" ")
-
- if trials % 100 == 0:
- print(
- '\rTraining {} Agents.\t Episode {}\t Average Score: {:.3f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
- env.get_num_agents(),
- trials,
- np.mean(scores_window),
- 100 * np.mean(done_window),
- eps,
- action_prob / np.sum(action_prob)))
- torch.save(agent.qnetwork_local.state_dict(),
- './Nets/avoid_checkpoint' + str(trials) + '.pth')
- action_prob = [1] * action_size
-
-print("multi_agent_trainging.py (2)")
-
-if __name__ == '__main__':
- print("main")
- main(sys.argv[1:])
-
-print("multi_agent_trainging.py (3)")
\ No newline at end of file
diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index 4f823be331850668e18bfdf66d35a915e3f6ccdc..280841c9e918896133290f625e69fc948fa0b52a 100644
--- a/torch_training/multi_agent_training.py
+++ b/torch_training/multi_agent_training.py
@@ -1,10 +1,10 @@
-import getopt
-import random
import sys
from collections import deque
+import getopt
import matplotlib.pyplot as plt
import numpy as np
+import random
import torch
from importlib_resources import path
@@ -17,8 +17,6 @@ from flatland.utils.rendertools import RenderTool
from torch_training.dueling_double_dqn import Agent
from utils.observation_utils import norm_obs_clip, split_tree
-print("multi_agent_trainging.py (1)")
-
def main(argv):
try:
@@ -29,7 +27,6 @@ def main(argv):
for opt, arg in opts:
if opt in ('-n', '--n_trials'):
n_trials = int(arg)
- print("main1")
random.seed(1)
np.random.seed(1)
@@ -64,8 +61,6 @@ def main(argv):
state_size = features_per_node * 85 * 2
action_size = 5
- print("main3")
-
# We set the number of episodes we would like to train on
if 'n_trials' not in locals():
n_trials = 30000
@@ -91,7 +86,6 @@ def main(argv):
record_images = False
frame_step = 0
- print("Going to run training for {} trials...".format(n_trials))
for trials in range(1, n_trials + 1):
if trials % 50 == 0 and not demo:
@@ -221,10 +215,5 @@ def main(argv):
plt.show()
-print("multi_agent_trainging.py (2)")
-
if __name__ == '__main__':
- print("main")
main(sys.argv[1:])
-
-print("multi_agent_trainging.py (3)")
diff --git a/tox.ini b/tox.ini
index 36b7c10a081b629375b202ad1e642e7366db2abe..da528553c74be43bc54ce7858bb057e117cee11a 100644
--- a/tox.ini
+++ b/tox.ini
@@ -22,8 +22,7 @@ passenv =
deps =
-r{toxinidir}/requirements_torch_training.txt
commands =
- python -m pip install -r requirements_torch_training.txt
- python torch_training/bla.py --n_trials=10
+ python torch_training/multi_agent_training.py --n_trials=10
[flake8]
max-line-length = 120