diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index 7b9470d3b980657073af02e87b92edfea98bd879..38f7c1caf7daa27bfd0bb1d5808dd027d0fea69b 100644
--- a/torch_training/multi_agent_training.py
+++ b/torch_training/multi_agent_training.py
@@ -1,3 +1,5 @@
+import getopt
+import sys
from collections import deque
import matplotlib.pyplot as plt
@@ -15,184 +17,198 @@ 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)
-
-"""
-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))
-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
-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
-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):
+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)
+ 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))
+ 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
+ # 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
+ 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.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
+ 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()):
- if demo:
- eps = 0
- # action = agent.act(np.array(obs[a]), eps=eps)
- action = agent.act(agent_obs[a], eps=eps)
- 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()):
- data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
+ 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)
- next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
- time_obs.append(next_obs)
-
- # Update replay buffer and train agent
+ 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_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
+ 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)
+ action = agent.act(agent_obs[a], eps=eps)
+ 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()):
- 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:
+ 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.\t Episode {}\t Average Score: {:.3f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
- env.get_num_agents(),
+ '\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)))
- torch.save(agent.qnetwork_local.state_dict(),
- './Nets/avoid_checkpoint' + str(trials) + '.pth')
- action_prob = [1] * action_size
-plt.plot(scores)
-plt.show()
+ 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
+ plt.plot(scores)
+ plt.show()
+
+if __name__ == '__main__':
+ main(sys.argv[1:])
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index 3096c8766e7fb953c855bb5b95d40794f2ee00f2..f575510f6e0e32eeaee2d29b7f5da0ced852fb81 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.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 dueling_double_dqn import Agent
@@ -16,15 +16,14 @@ from utils.observation_utils import norm_obs_clip, split_tree
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 = arg
+ if opt in ('-n', '--n_trials'):
+ n_trials = int(arg)
random.seed(1)
np.random.seed(1)