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Erik Nygren authoredErik Nygren authored
multi_agent_training.py 10.91 KiB
# Import packages for plotting and system
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 and utility functions to normalize observation
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 sparse_rail_generator
# Import Flatland/ Observations and Predictors
from flatland.envs.schedule_generators import sparse_schedule_generator
from torch_training.dueling_double_dqn import Agent
from utils.observation_utils import normalize_observation
def main(argv):
try:
opts, args = getopt.getopt(argv, "n:", ["n_episodes="])
except getopt.GetoptError:
print('training_navigation.py -n <n_episodes>')
sys.exit(2)
for opt, arg in opts:
if opt in ('-n', '--n_episodes'):
n_episodes = int(arg)
## Initialize the random
random.seed(1)
np.random.seed(1)
# Initialize a random map with a random number of agents
"""
Get an observation builder and predictor:
The predictor will always predict the shortest path from the current location of the agent.
This is used to warn for potential conflicts --> Should be enhanced to get better performance!
"""
# Parameters for the Environment
x_dim = 20
y_dim = 20
n_agents = 5
tree_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
predictor = ShortestPathPredictorForRailEnv()
observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=predictor)
# 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=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=observation_helper)
env.reset(True, True)
handle = env.get_agent_handles()
num_features_per_node = env.obs_builder.observation_dim
nr_nodes = 0
for i in range(tree_depth + 1):
nr_nodes += np.power(4, i)
state_size = num_features_per_node * nr_nodes
action_size = 5
# We set the number of episodes we would like to train on
if 'n_episodes' not in locals():
n_episodes = 60000
# Set max number of steps per episode as well as other training relevant parameter
max_steps = int((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)
scores = []
dones_list = []
action_prob = [0] * action_size
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
observation_radius = 10
# Initialize the agent
agent = Agent(state_size, action_size, "FC", 0)
# Here you can pre-load an agent
if False:
with path(torch_training.Nets, "avoid_checkpoint2400.pth") as file_in:
agent.qnetwork_local.load_state_dict(torch.load(file_in))
# Do training over n_episodes
for episodes in range(1, n_episodes + 1):
"""
Training Curriculum: In order to get good generalization we change the number of agents
and the size of the levels every 50 episodes.
"""
if episodes % 50 == 1:
env = RailEnv(width=x_dim,
height=y_dim,
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=observation_helper)
# Adjust the parameters according to the new env.
max_steps = int((env.height + env.width))
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
# Reset environment
obs = env.reset(True, True)
# Setup placeholder for finals observation of a single agent. This is necessary because agents terminate at
# different times during an episode
final_obs = agent_obs.copy()
final_obs_next = agent_next_obs.copy()
# Build agent specific observations
for a in range(env.get_num_agents()):
agent_obs[a] = agent_obs[a] = normalize_observation(obs[a], observation_radius=10)
score = 0
env_done = 0
# Run episode
for step in range(max_steps):
# Action
for a in range(env.get_num_agents()):
if env.agents[a].speed_data['position_fraction'] == 0.:
register_action_state[a] = True
else:
register_action_state[a] = False
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)
# Build agent specific observations and normalize
for a in range(env.get_num_agents()):
agent_next_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
# Update replay buffer and train agent
for a in range(env.get_num_agents()):
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 done[a] and register_action_state[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()
# Copy observation
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
# Collection information about training
tasks_finished = 0
for _idx in range(env.get_num_agents()):
if done[_idx] == 1:
tasks_finished += 1
done_window.append(tasks_finished / env.get_num_agents())
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,
episodes,
np.mean(scores_window),
100 * np.mean(done_window),
eps, action_prob / np.sum(action_prob)), end=" ")
if episodes % 100 == 0:
print(
'\rTraining {} Agents.\t Episode {}\t Average Score: {:.3f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
env.get_num_agents(),
episodes,
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(episodes) + '.pth')
action_prob = [1] * action_size
plt.plot(scores)
plt.show()
if __name__ == '__main__':
main(sys.argv[1:])