Getting Started¶
Overview¶
Following are three short tutorials to help new users get acquainted with how to create RailEnvs, how to train simple DQN agents on them, and how to customize them.
To use flatland in a project:
import flatland
Part 1 : Basic Usage¶
The basic usage of RailEnv environments consists in creating a RailEnv object endowed with a rail generator, that generates new rail networks on each reset, and an observation generator object, that is supplied with environment-specific information at each time step and provides a suitable observation vector to the agents.
The simplest rail generators are envs.rail_generators.rail_from_manual_specifications_generator and envs.rail_generators.random_rail_generator.
The first one accepts a list of lists whose each element is a 2-tuple, whose entries represent the ‘cell_type’ (see core.transitions.RailEnvTransitions) and the desired clockwise rotation of the cell contents (0, 90, 180 or 270 degrees). For example,
specs = [[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
[(0, 0), (0, 0), (0, 0), (0, 0), (7, 0), (0, 0)],
[(7, 270), (1, 90), (1, 90), (1, 90), (2, 90), (7, 90)],
[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]]
env = RailEnv(width=6,
height=4,
rail_generator=rail_from_manual_specifications_generator(specs),
number_of_agents=1,
obs_builder_object=TreeObsForRailEnv(max_depth=2))
Alternatively, a random environment can be generated (optionally specifying weights for each cell type to increase or decrease their proportion in the generated rail networks).
# Relative weights of each cell type to be used by the random rail generators.
transition_probability = [1.0, # empty cell - Case 0
1.0, # Case 1 - straight
1.0, # Case 2 - simple switch
0.3, # Case 3 - diamond drossing
0.5, # Case 4 - single slip
0.5, # Case 5 - double slip
0.2, # Case 6 - symmetrical
0.0, # Case 7 - dead end
0.2, # Case 8 - turn left
0.2, # Case 9 - turn right
1.0] # Case 10 - mirrored switch
# Example generate a random rail
env = RailEnv(width=10,
height=10,
rail_generator=random_rail_generator(
cell_type_relative_proportion=transition_probability
),
number_of_agents=3,
obs_builder_object=TreeObsForRailEnv(max_depth=2))
Environments can be rendered using the utils.rendertools utilities, for example:
env_renderer = RenderTool(env)
env_renderer.render_env(show=True)
Finally, the environment can be run by supplying the environment step function with a dictionary of actions whose keys are agents’ handles (returned by env.get_agent_handles() ) and the corresponding values the selected actions. For example, for a 2-agents environment:
handles = env.get_agent_handles()
action_dict = {handles[0]:0, handles[1]:0}
obs, all_rewards, done, _ = env.step(action_dict)
where ‘obs’, ‘all_rewards’, and ‘done’ are also dictionary indexed by the agents’ handles, whose values correspond to the relevant observations, rewards and terminal status for each agent. Further, the ‘dones’ dictionary returns an extra key ‘__all__’ that is set to True after all agents have reached their goals.
In the specific case a TreeObsForRailEnv observation builder is used, it is possible to print a representation of the returned observations with the following code. Also, tree observation data is displayed by RenderTool by default.
for i in range(env.get_num_agents()):
env.obs_builder.util_print_obs_subtree(
tree=obs[i],
num_features_per_node=5
)
The complete code for this part of the Getting Started guide can be found in
Part 2 : Training a Simple an Agent on Flatland¶
This is a brief tutorial on how to train an agent on Flatland. Here we use a simple random agent to illustrate the process on how to interact with the environment. The corresponding code can be found in examples/training_example.py and in the baselines repository you find a tutorial to train a DQN agent to solve the navigation task.
We start by importing the necessary Flatland libraries
from flatland.envs.rail_generators import complex_rail_generator
from flatland.envs.schedule_generators import complex_schedule_generator
from flatland.envs.rail_env import RailEnv
The complex_rail_generator is used in order to guarantee feasible railway network configurations for training. Next we configure the difficulty of our task by modifying the complex_rail_generator parameters.
env = RailEnv( width=15,
height=15,
rail_generator=complex_rail_generator(
nr_start_goal=10,
nr_extra=10,
min_dist=10,
max_dist=99999,
seed=0),
number_of_agents=5)
The difficulty of a railway network depends on the dimensions (width x height) and the number of agents in the network. By varying the number of start and goal connections (nr_start_goal) and the number of extra railway elements added (nr_extra) the number of alternative paths of each agents can be modified. The more possible paths an agent has to reach its target the easier the task becomes. Here we don’t specify any observation builder but rather use the standard tree observation. If you would like to use a custom obervation please follow the instructions in the next tutorial. Feel free to vary these parameters to see how your own agent holds up on different setting. The evalutation set of railway configurations will cover the whole spectrum from easy to complex tasks.
Once we are set with the environment we can load our preferred agent from either RLlib or any other ressource. Here we use a random agent to illustrate the code.
agent = RandomAgent(env.action_space, env.observation_space)
We start every trial by resetting the environment
obs = env.reset()
Which provides the initial observation for all agents (obs = array of all observations). In order for the environment to step forward in time we need a dictionar of actions for all active agents.
for handle in range(env.get_num_agents()):
action = agent.act(obs[handle])
action_dict.update({handle: action})
This dictionary is then passed to the environment which checks the validity of all actions and update the environment state.
next_obs, all_rewards, done, _ = env.step(action_dict)
The environment returns an array of new observations, reward dictionary for all agents as well as a flag for which agents are done. This information can be used to update the policy of your agent and if done[‘__all__’] == True the episode terminates.
Part 3 : Customizing Observations and Level Generators¶
Example code for generating custom observations given a RailEnv and to generate random rail maps are available in examples/custom_observation_example.py and examples/custom_railmap_example.py .
Custom observations can be produced by deriving a new object from the core.env_observation_builder.ObservationBuilder base class, for example as follows:
class CustomObs(ObservationBuilder):
def __init__(self):
self.observation_space = [5]
def reset(self):
return
def get(self, handle):
observation = handle*np.ones((5,))
return observation
It is important that an observation_space is defined with a list of dimensions of the returned observation tensors. get() returns the observation for each agent, of handle ‘handle’.
A RailEnv environment can then be created as usual:
env = RailEnv(width=7,
height=7,
rail_generator=random_rail_generator(),
number_of_agents=3,
obs_builder_object=CustomObs())
As for generating custom rail maps, the RailEnv class accepts a rail_generator argument that must be a function with arguments width, height, num_agents, and num_resets=0, and that has to return a GridTransitionMap object (the rail map), and three lists of tuples containing the (row,column) coordinates of each of num_agent agents, their initial orientation (0=North, 1=East, 2=South, 3=West), and the position of their targets.
For example, the following custom rail map generator returns an empty map of size (height, width), with no agents (regardless of num_agents):
def custom_rail_generator():
def generator(width, height, num_agents=0, num_resets=0):
rail_trans = RailEnvTransitions()
grid_map = GridTransitionMap(width=width, height=height, transitions=rail_trans)
rail_array = grid_map.grid
rail_array.fill(0)
agents_positions = []
agents_direction = []
agents_target = []
return grid_map, agents_positions, agents_direction, agents_target
return generator
It is worth to note that helpful utilities to manage RailEnv environments and their related data structures are available in ‘envs.env_utils’. In particular, envs.env_utils.get_rnd_agents_pos_tgt_dir_on_rail is fairly handy to fill in random (but consistent) agents along with their targets and initial directions, given a rail map (GridTransitionMap object) and the desired number of agents:
agents_position, agents_direction, agents_target = get_rnd_agents_pos_tgt_dir_on_rail(
rail_map,
num_agents)