Changelog

Changes since Flatland 2.1.0

Changes in ‘schedule_generators’

  • Schedule generators now provide the max number of steps allowed per episode
  • Pickle files generated with older versions of Flatland need to be regenerated in order to include _max_episode_steps Changes since Flatland 2.0.0

Changes in EnvAgent

  • class EnvAgentStatic was removed, so there is only class EnvAgent left which should simplify the handling of agents. The member self.agents_static of RailEnv was therefore also removed. Old Scence saved as pickle files cannot be loaded anymore.

Changes in malfunction behavior

  • agent attribute next_malfunctionis not used anymore, it will be removed fully in future versions.
  • break_agent() function is introduced which induces malfunctions in agent according to poisson process
  • _fix_agent_after_malfunction() fixes agents after attribute malfunction == 0
  • Introduced the concept of malfunction generators. Here you can add different malfunction models in future updates. Currently it only loads from files and parameters.

Changes in Environment

  • moving of member variable distance_map_computed to new class DistanceMap

Changes in rail generator and RailEnv

  • renaming of distance_maps into distance_map
  • by default the reset method of RailEnv is not called in the constructor of RailEnv anymore (compliance for OpenAI Gym). Therefore the reset method needs to be called after the creation of a RailEnv object
  • renaming of parameters RailEnv.reset(): from regen_rail to regenerate_rail, from replace_agents to regenerate_schedule

Changes in schedule generation

  • return value of schedule generator has changed to the named tuple Schedule. From the point of view of a consumer, nothing has changed, this is just a type hint which is introduced where the attributes of Schedule have names.

Changes since Flatland 1.0.0

Changes in stock predictors

The stock ShortestPathPredictorForRailEnv now respects the different agent speeds and updates their prediction accordingly.

Changes in stock observation biulders

  • TreeObsForRailEnv now has 11 features!
    • 10th feature now indicates if a malfunctioning agent has been detected and how long the malfunction will still be present
    • 11th feautre now indicates the minimal observed fractional speed of agents traveling in the same direction
  • GlobalObsForRailEnv now has new features!
    • Targets and other agent targets still represented in same way
    • obs_agents_state now contains 4 channels
      • 0th channel -> agent direction at agent position
      • 1st channel -> other agents direction at their positions
      • 2nd channel -> all agent malfunction duration at their positions
      • 3rd channel -> all agent fractional speeds at their positions
  • LocalObsForRailEnv was not update to Flatland 2.0 because it was never used by participants of the challenge.

Changes in level generation

  • Separation of schedule_generator from rail_generator:
    • Renaming of flatland/envs/generators.py to flatland/envs/rail_generators.py
    • rail_generator now only returns the grid and optionally hints (a python dictionary); the hints are currently use for distance_map and communication of start and goal position in complex rail generator.
    • schedule_generator takes a GridTransitionMap and the number of agents and optionally the agents_hints field of the hints dictionary.
    • Inrodcution of types hints:
RailGeneratorProduct = Tuple[GridTransitionMap, Optional[Any]]
RailGenerator = Callable[[int, int, int, int], RailGeneratorProduct]
AgentPosition = Tuple[int, int]
ScheduleGeneratorProduct = Tuple[List[AgentPosition], List[AgentPosition], List[AgentPosition], List[float]]
ScheduleGenerator = Callable[[GridTransitionMap, int, Optional[Any]], ScheduleGeneratorProduct]

Multi Speed

  • Different agent speeds are introduced. Agents now travel at a max speed which is a fraction. Meaning that they only advance parts within a cell and need several steps to move to the next cell.
    • Fastest speed is 1. At this speed an agent can move to a new cell at each time step t.
    • Slower speeds are smaller than one. At each time step an agent moves the fraction of its speed forward within a cell. It only changes cell when it’s fractional position is greater or equal to 1.
    • Multi-speed introduces the challenge of ordering the trains correctly when traveling in the same direction.
  • Agents always travel at their full speed when moving.

To set up multiple speeds you have to modify the agent.speed_data within your schedule_generator. See this file for a good example.

ATTENTION multi speed means that the agents actions are not registered on every time step. Only at new cell entry can new actions be chosen! Beware to respect this with your controller as actions are only important at the specific time steps! This is shown as an example in the navigation training

Stochastic events

Just like in real-worl transportation systems we introduced stochastic events to disturb normal traffic flow. Currently we implemented a malfunction process that stops agents at random time intervalls for a random time of duration. Currently the Flatland environment can be initiated with the following poisson process parameters:

# 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
                   }

The duration of a malfunction is uniformly drawn from the intervall [min_duration,max_duration0] and the occurance of malfunctions follows a point poisson process with mean rate malfunctin_rate.

!!!!IMPORTANT!!!! Once a malfunction duration has finished, the agent will automatically resume movement. This is important because otherwise it can get stuck in fractional positions and your code might forget to restart the agent at the first possible time. Therefore this has been automated. You can however stop the agent again at the next cell. This might in rare occasions lead to unexpected behavior, we are looking into this and will push a fix soon.

Baselines repository

The baselines repository is not yet fully updated to handle multi-speed and stochastic events. Training needs to be modified to omitt all states inbetween the states where an agent can chose an action. Simple navigation training is already up to date. See here for more details.

Changes since Flatland 0.2

Please list all major changes since the last version:

  • Refactoring of rendering code: CamelCase functions changed to snake_case
  • Tree Observation Added a new Featuer: unusable_switch which indicates switches that are not branchingpoints for the observing agent
  • Updated the shortest path predictor
  • Updated conflict detection with predictor
  • Episodes length can be set as maximum number of steps allowed.

Flatland 2.0 Introduction

What’s new?

In this version of Flatland, we are moving closer to realistic and more complex railway problems. Earlier versions of Flatland introduced you to the concept of restricted transitions, but they were still too simplistic to give us feasible solutions for daily operations. Thus the following changes are coming in the next version to be closer to real railway network challenges:

  • New Level Generator provide less connections between different nodes in the network and thus agent densities on rails are much higher.
  • Stochastic Events cause agents to stop and get stuck for different numbers of time steps.
  • Different Speed Classes allow agents to move at different speeds and thus enhance complexity in the search for optimal solutions.

We explain these changes in more detail and how you can play with their parametrization in Tutorials 3–5:

We appreciate your feedback on the performance and the difficulty on these levels to help us shape the best possible Flatland 2.0 environment.

Example code

To see all the changes in action you can just run the

example.