try_graph.py 12.9 KB
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import numpy as np
import time

# In Flatland you can use custom observation builders and predicitors
# Observation builders generate the observation needed by the controller
# Preditctors can be used to do short time prediction which can help in avoiding conflicts in the network
from flatland.envs.observations import GlobalObsForRailEnv
# First of all we import the Flatland rail environment
from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_env import RailEnvActions, RailAgentStatus
from flatland.envs.rail_generators import sparse_rail_generator
from flatland.envs.schedule_generators import sparse_schedule_generator
# We also include a renderer because we want to visualize what is going on in the environment
from flatland.utils.rendertools import RenderTool, AgentRenderVariant
from flatland.envs.malfunction_generators import malfunction_from_params

from libs.graph import BuildGraphFromEnvironment, GraphPathsLocker
from libs.graph_agent import GraphAgent, AgentsList

import os

width = 40  # With of map
height = 40  # Height of map
nr_trains = 8  # Number of trains that have an assigned task in the env
cities_in_map = 5  # Number of cities where agents can start or end
seed = 14  # Random seed

width = 150  # With of map
height = 150  # Height of map
nr_trains = 100  # Number of trains that have an assigned task in the env
cities_in_map = 100  # Number of cities where agents can start or end
seed = 14  # Random seed

# width = 26  # With of map
# height = 26  # Height of map
# nr_trains = 1  # Number of trains that have an assigned task in the env
# cities_in_map = 2  # Number of cities where agents can start or end
# seed = 14  # Random seed

# width = 40  # With of map
# height = 40  # Height of map
# nr_trains = 5  # Number of trains that have an assigned task in the env
# cities_in_map = 5  # Number of cities where agents can start or end
# seed = 14  # Random seed

# width = 30  # With of map
# height = 30  # Height of map
# nr_trains = 3  # Number of trains that have an assigned task in the env
# cities_in_map = 100  # Number of cities where agents can start or end
# seed = 14  # Random seed

width = 80  # With of map
height = 80  # Height of map
nr_trains = 50  # Number of trains that have an assigned task in the env
cities_in_map = 100  # Number of cities where agents can start or end
seed = 14  # Random seed


grid_distribution_of_cities = False  # Type of city distribution, if False cities are randomly placed
max_rails_between_cities = 2  # Max number of tracks allowed between cities. This is number of entry point to a city
max_rail_in_cities = 6  # Max number of parallel tracks within a city, representing a realistic trainstation

rail_generator = sparse_rail_generator(max_num_cities=cities_in_map,
                                       seed=seed,
                                       grid_mode=grid_distribution_of_cities,
                                       max_rails_between_cities=max_rails_between_cities,
                                       max_rails_in_city=max_rail_in_cities,
                                       )

# The schedule generator can make very basic schedules with a start point, end point and a speed profile for each agent.
# The speed profiles can be adjusted directly as well as shown later on. We start by introducing a statistical
# distribution of speed profiles

# 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

# We can now initiate the schedule generator with the given speed profiles

schedule_generator = sparse_schedule_generator(speed_ration_map)

# We can furthermore pass stochastic data to the RailEnv constructor which will allow for stochastic malfunctions
# during an episode.

stochastic_data = {'malfunction_rate': 100,  # Rate of malfunction occurence of single agent
                   'prop_malfunction': 0.01,
                   'min_duration': 15,  # Minimal duration of malfunction
                   'max_duration': 50  # Max duration of malfunction
                   }


# Custom observation builder without predictor
observation_builder = GlobalObsForRailEnv()

# Custom observation builder with predictor, uncomment line below if you want to try this one
# observation_builder = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())

# Construct the enviornment with the given observation, generataors, predictors, and stochastic data
env = RailEnv(width=width,
              height=height,
              rail_generator=rail_generator,
              schedule_generator=schedule_generator,
              number_of_agents=nr_trains,
              malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),  # Malfunction data generator
              obs_builder_object=observation_builder,
              remove_agents_at_target=True  # Removes agents at the end of their journey to make space for others
              )
env.reset()

# Initiate the renderer
env_renderer = RenderTool(env, gl="PILSVG",
                          agent_render_variant=AgentRenderVariant.AGENT_SHOWS_OPTIONS_AND_BOX,
                          show_debug=False,
                          screen_height=1920,  # Adjust these parameters to fit your resolution
                          screen_width=1080)  # Adjust these parameters to fit your resolution

# The first thing we notice is that some agents don't have feasible paths to their target.
# We first look at the map we have created

# nv_renderer.render_env(show=True)


timev = time.time()
graph = BuildGraphFromEnvironment(env)
locker = GraphPathsLocker(env.height, env.width)
controllers = [GraphAgent(graph.vs, graph.es, graph.rev_es, graph.calc_distances(agent.target), agent.initial_position, agent.direction, agent.target, locker, env=env, agent_id=i) for i, agent in enumerate(env.agents)]

alist = AgentsList(controllers, env.agents, max(5, int(round(0.1*(env.width+env.height)/2))))

print("Time for graph and agents:", time.time()-timev)

# We start by looking at the information of each agent
# We can see the task assigned to the agent by looking at
print("\n Agents in the environment have to solve the following tasks: \n")
for agent_idx, agent in enumerate(env.agents):
    print(
        "The agent with index {} has the task to go from its initial position {}, facing in the direction {} to its target at {}.".format(
            agent_idx, agent.initial_position, agent.direction, agent.target))

# The agent will always have a status indicating if it is currently present in the environment or done or active
# For example we see that agent with index 0 is currently not active
print("\n Their current statuses are:")
print("============================")

for agent_idx, agent in enumerate(env.agents):
    print("Agent {} status is: {} with its current position being {}".format(agent_idx, str(agent.status),
                                                                             str(agent.position)))

# The agent needs to take any action [1,2,3] except do_nothing or stop to enter the level
# If the starting cell is free they will enter the level
# If multiple agents want to enter the same cell at the same time the lower index agent will enter first.

# Let's check if there are any agents with the same start location
agents_with_same_start = set()
print("\n The following agents have the same initial position:")
print("=====================================================")
for agent_idx, agent in enumerate(env.agents):
    for agent_2_idx, agent2 in enumerate(env.agents):
        if agent_idx != agent_2_idx and agent.initial_position == agent2.initial_position:
            print("Agent {} as the same initial position as agent {}".format(agent_idx, agent_2_idx))
            agents_with_same_start.add(agent_idx)

# Lets try to enter with all of these agents at the same time
action_dict = dict()

# for agent_id in agents_with_same_start:
#     action_dict[agent_id] = 1  # Try to move with the agents

# Do a step in the environment to see what agents entered:
# env.step(action_dict)

# Current state and position of the agents after all agents with same start position tried to move
# print("\n This happened when all tried to enter at the same time:")
# print("========================================================")
# for agent_id in agents_with_same_start:
#     print(
#         "Agent {} status is: {} with the current position being {}.".format(
#             agent_id, str(env.agents[agent_id].status),
#             str(env.agents[agent_id].position)))

# As you see only the agents with lower indexes moved. As soon as the cell is free again the agents can attempt
# to start again.

# You will also notice, that the agents move at different speeds once they are on the rail.
# The agents will always move at full speed when moving, never a speed inbetween.
# The fastest an agent can go is 1, meaning that it moves to the next cell at every time step
# All slower speeds indicate the fraction of a cell that is moved at each time step
# Lets look at the current speed data of the agents:

print("\n The speed information of the agents are:")
print("=========================================")

for agent_idx, agent in enumerate(env.agents):
    print(
        "Agent {} speed is: {:.2f} with the current fractional position being {}".format(
            agent_idx, agent.speed_data['speed'], agent.speed_data['position_fraction']))

# New the agents can also have stochastic malfunctions happening which will lead to them being unable to move
# for a certain amount of time steps. The malfunction data of the agents can easily be accessed as follows
print("\n The malfunction data of the agents are:")
print("========================================")

for agent_idx, agent in enumerate(env.agents):
    print(
        "Agent {} is OK = {}".format(
            agent_idx, agent.malfunction_data['malfunction'] < 1))

# Now that you have seen these novel concepts that were introduced you will realize that agents don't need to take
# an action at every time step as it will only change the outcome when actions are chosen at cell entry.
# Therefore the environment provides information about what agents need to provide an action in the next step.
# You can access this in the following way.

# Chose an action for each agent
# for a in range(env.get_num_agents()):
#     action = controller.act(0)
#     action_dict.update({a: action})

# for i, a in enumerate(env.agents):
#     action = controllers[i].act(a)
#     action_dict.update({i: action})

# Do the environment step
observations, rewards, dones, information = env.step(action_dict)
print("\n The following agents can register an action:")
print("========================================")
for info in information['action_required']:
    print("Agent {} needs to submit an action.".format(info))

# We recommend that you monitor the malfunction data and the action required in order to optimize your training
# and controlling code.

# Let us now look at an episode playing out with random actions performed

print("\nStart episode...")

# Reset the rendering system
env_renderer.reset()

# Here you can also further enhance the provided observation by means of normalization
# See training navigation example in the baseline repository


score = 0
# Run episode
frame_step = 0

# for step in range(500):
step = 0
while True:
    step += 1
    # Chose an action for each agent in the environment
    # for a in range(env.get_num_agents()):
    #     action = controller.act(observations[a])
    #     action_dict.update({a: action})

    # for i, a in enumerate(env.agents):
    for i in alist.active():
        a = env.agents[i]

        if (a.speed_data['position_fraction']==0.0):
            action = controllers[i].act(a)
            action_dict.update({i: action})

        # env.agents[a].position = env.agents[a].target

    # Environment step which returns the observations for all agents, their corresponding
    # reward and whether their are done

    next_obs, all_rewards, done, _ = env.step(action_dict)

    # env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
    env_renderer.render_env(show=True, show_observations=True, show_predictions=True)

    # os.makedirs('./misc/Fames2/', exist_ok=True)
    # env_renderer.gl.save_image('./misc/Fames2/flatland_frame_{:04d}.png'.format(step))
    frame_step += 1

    score += np.sum(list(all_rewards.values()))

    #
    # observations = next_obs.copy()
    if done['__all__']:
        break
    finished = np.sum([a.status==RailAgentStatus.DONE or a.status==RailAgentStatus.DONE_REMOVED for a in env.agents])
    print('Episode: Steps {}\t Score = {}\t Finished = {}\t Not started = {}'.format(step, score, finished, alist.not_started()))


finished = np.sum([a.status==RailAgentStatus.DONE or a.status==RailAgentStatus.DONE_REMOVED for a in env.agents])
print(f'Trains finished {finished}/{len(env.agents)} = {finished*100/len(env.agents):.2f}%')