intro_observationbuilder.rst

Getting Started with custom observations

Overview

One of the main objectives of the Flatland-Challenge is to find a suitable observation (relevant features for the problem at hand) to solve the task. Therefore Flatland was built with as much flexibility as possible when it comes to building your custom observations: observations in Flatland environments are fully customizable. Whenever an environment needs to compute new observations for each agent, it queries an object derived from the ObservationBuilder base class, which takes the current state of the environment and returns the desired observation.

Example 1 : Simple (but useless) observation

In this first example we implement all the functions necessary for the observation builder to be valid and work with Flatland. Custom observation builder objects need to derive from the flatland.core.env_observation_builder.ObservationBuilder base class and must implement two methods, reset(self) and get(self, handle).

Below is a simple example that returns observation vectors of size observation_space = 5 featuring only the ID (handle) of the agent whose observation vector is being computed:

class SimpleObs(ObservationBuilder):
    """
    Simplest observation builder. The object returns observation vectors with 5 identical components,
    all equal to the ID of the respective agent.
    """
    def __init__(self):
        self.observation_space = [5]

    def reset(self):
        return

    def get(self, handle):
        observation = handle * np.ones((self.observation_space[0],))
        return observation

We can pass an instance of our custom observation builder SimpleObs to the RailEnv creator as follows:

env = RailEnv(width=7,
              height=7,
              rail_generator=random_rail_generator(),
              number_of_agents=3,
              obs_builder_object=SimpleObs())

Anytime env.reset() or env.step() is called, the observation builder will return the custom observation of all agents initialized in the env. In the next example we highlight how to derive from existing observation builders and how to access internal variables of Flatland.

Example 2 : Single-agent navigation

Observation builder objects can of course derive from existing concrete subclasses of ObservationBuilder. For example, it may be useful to extend the TreeObsForRailEnv observation builder. A feature of this class is that on reset(), it pre-computes the lengths of the shortest paths from all cells and orientations to the target of each agent, i.e. a distance map for each agent.

In this example we exploit these distance maps by implementing an observation builder that shows the current shortest path for each agent as a one-hot observation vector of length 3, whose components represent the possible directions an agent can take (LEFT, FORWARD, RIGHT). All values of the observation vector are set to 0 except for the shortest direction where it is set to 1.

Using this observation with highly engineered features indicating the agent's shortest path, an agent can then learn to take the corresponding action at each time-step; or we could even hardcode the optimal policy. Note that this simple strategy fails when multiple agents are present, as each agent would only attempt its greedy solution, which is not usually Pareto-optimal in this context.

from flatland.envs.observations import TreeObsForRailEnv

class SingleAgentNavigationObs(TreeObsForRailEnv):
    """
    We derive our observation builder from TreeObsForRailEnv, to exploit the existing implementation to compute
    the minimum distances from each grid node to each agent's target.

    We then build a representation vector with 3 binary components, indicating which of the 3 available directions
    for each agent (Left, Forward, Right) lead to the shortest path to its target.
    E.g., if taking the Left branch (if available) is the shortest route to the agent's target, the observation vector
    will be [1, 0, 0].
    """
    def __init__(self):
        super().__init__(max_depth=0)
        # We set max_depth=0 in because we only need to look at the current
        # position of the agent to decide what direction is shortest.
        self.observation_space = [3]

    def reset(self):
        # Recompute the distance map, if the environment has changed.
        super().reset()

    def get(self, handle):
        # Here we access agent information from the environment.
        # Information from the environment can be accessed but not changed!
        agent = self.env.agents[handle]

        possible_transitions = self.env.rail.get_transitions(*agent.position, agent.direction)
        num_transitions = np.count_nonzero(possible_transitions)

        # Start from the current orientation, and see which transitions are available;
        # organize them as [left, forward, right], relative to the current orientation
        # If only one transition is possible, the forward branch is aligned with it.
        if num_transitions == 1:
            observation = [0, 1, 0]
        else:
            min_distances = []
            for direction in [(agent.direction + i) % 4 for i in range(-1, 2)]:
                if possible_transitions[direction]:
                    new_position = self._new_position(agent.position, direction)
                    min_distances.append(self.distance_map[handle, new_position[0], new_position[1], direction])
                else:
                    min_distances.append(np.inf)

            observation = [0, 0, 0]
            observation[np.argmin(min_distances)] = 1

        return observation

env = RailEnv(width=7,
              height=7,
              rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, \
                min_dist=8, max_dist=99999, seed=0),
              number_of_agents=2,
              obs_builder_object=SingleAgentNavigationObs())

obs, all_rewards, done, _ = env.step({0: 0, 1: 1})
for i in range(env.get_num_agents()):
    print(obs[i])

Finally, the following is an example of hard-coded navigation for single agents that achieves optimal single-agent navigation to target, and shows the path taken as an animation.

env = RailEnv(width=50,
              height=50,
              rail_generator=random_rail_generator(),
              number_of_agents=1,
              obs_builder_object=SingleAgentNavigationObs())

obs, all_rewards, done, _ = env.step({0: 0})

env_renderer = RenderTool(env, gl="PILSVG")
env_renderer.render_env(show=True, frames=True, show_observations=False)

for step in range(100):
    action = np.argmax(obs[0])+1
    obs, all_rewards, done, _ = env.step({0:action})
    print("Rewards: ", all_rewards, "  [done=", done, "]")

    env_renderer.render_env(show=True, frames=True, show_observations=False)
    time.sleep(0.1)

The code examples above appear in the example file custom_observation_example.py. You can run it using python examples/custom_observation_example.py from the root folder of the flatland repo. The two examples are run one after the other.

Example 3 : Using custom predictors and rendering observation

Because the re-scheduling task of the Flatland-Challenge requires some short time planning we allow the possibility to use custom predictors that help predict upcoming conflicts and help agent solve them in a timely manner. In the Flatland Environment we included an initial predictor ShortestPathPredictorForRailEnv to give you an idea what you can do with these predictors.

Any custom predictor can be passed to the observation builder and then be used to build the observation. In this example we illustrate how an observation builder can be used to detect conflicts using a predictor.

The observation is incomplete as it only contains information about potential conflicts and has no feature about the agent objectives.

In addition to using your custom predictor you can also make your custom observation ready for rendering. (This can be done in a similar way for your predictor). All you need to do in order to render your custom observation is to populate self.env.dev_obs_dict[handle] for every agent (all handles). (For the predictor use self.env.dev_pred_dict[handle]).

class ObservePredictions(TreeObsForRailEnv):

""" We use the provided ShortestPathPredictor to illustrate the usage of predictors in your custom observation.

We derive our observation builder from TreeObsForRailEnv, to exploit the existing implementation to compute the minimum distances from each grid node to each agent's target.

This is necessary so that we can pass the distance map to the ShortestPathPredictor

Here we also want to highlight how you can visualize your observation """

def __init__(self, predictor):

super().__init__(max_depth=0) self.observation_space = [10] self.predictor = predictor

def reset(self):

# Recompute the distance map, if the environment has changed. super().reset()

def get_many(self, handles=None):

''' Because we do not want to call the predictor seperately for every agent we implement the get_many function Here we can call the predictor just ones for all the agents and use the predictions to generate our observations :param handles: :return: '''

self.predictions = self.predictor.get(custom_args={'distance_map': self.distance_map})

self.predicted_pos = {} for t in range(len(self.predictions[0])):

pos_list = [] for a in handles:

pos_list.append(self.predictions[a][t][1:3])

# We transform (x,y) coodrinates to a single integer number for simpler comparison self.predicted_pos.update({t: coordinate_to_position(self.env.width, pos_list)})

observations = {}

# Collect all the different observation for all the agents for h in handles:

observations[h] = self.get(h)

return observations

def get(self, handle):

''' Lets write a simple observation which just indicates whether or not the own predicted path overlaps with other predicted paths at any time. This is useless for the task of navigation but might help when looking for conflicts. A more complex implementation can be found in the TreeObsForRailEnv class

Each agent recieves an observation of length 10, where each element represents a prediction step and its value is:

• 0 if no overlap is happening
• 1 where n i the number of other paths crossing the predicted cell

param handle:	handeled as an index of an agent
return:	Observation of handle

'''

observation = np.zeros(10)

# We are going to track what cells where considered while building the obervation and make them accesible # For rendering

visited = set() for _idx in range(10):

# Check if any of the other prediction overlap with agents own predictions x_coord = self.predictions[handle][_idx][1] y_coord = self.predictions[handle][_idx][2]

# We add every observed cell to the observation rendering visited.add((x_coord, y_coord)) if self.predicted_pos[_idx][handle] in np.delete(self.predicted_pos[_idx], handle, 0):

# We detect if another agent is predicting to pass through the same cell at the same predicted time observation[handle] = 1

# This variable will be access by the renderer to visualize the observation self.env.dev_obs_dict[handle] = visited

return observation

# Initiate the Predictor
CustomPredictor = ShortestPathPredictorForRailEnv(10)

# Pass the Predictor to the observation builder
CustomObsBuilder = ObservePredictions(CustomPredictor)

# Initiate Environment
env = RailEnv(width=10,
              height=10,
              rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=1, min_dist=8, max_dist=99999, seed=0),
              number_of_agents=3,
              obs_builder_object=CustomObsBuilder)

obs = env.reset()
env_renderer = RenderTool(env, gl="PILSVG")

# We render the initial step and show the obsered cells as colored boxes
env_renderer.render_env(show=True, frames=True, show_observations=True, show_predictions=False)

action_dict = {}
for step in range(100):
    for a in range(env.get_num_agents()):
        action = np.random.randint(0, 5)
        action_dict[a] = action
    obs, all_rewards, done, _ = env.step(action_dict)
    print("Rewards: ", all_rewards, "  [done=", done, "]")
    env_renderer.render_env(show=True, frames=True, show_observations=True, show_predictions=False)
    time.sleep(0.5)