This was an expected outcome and one of the reasons we chose to do the challenge setup with these distinct rounds. We highly encourage the use of both classical OR algorithms as well as novel RL solutions and thus set round 0 to be well suited for OR algorithms, whereas round 2 with introduced stochasticity is better suited for learning algorithms.
Round 1 instances can be solved using classical planning algorithms, because there is no stochasticity involved in the enivornment and the complexity is rather low due to the speed of all trains being equal. This round serves as a kind of benchmark for RL approaches to see if they can achieve similar results as classical approaches.
Round 2 will differ from round 1 in that there are stochastic events occuring during an episode, so pre-planned trajectories will not be feasible anymore. Also the introduction of different speed profiles for different agents will enhance complexity.
I understand that tuning the optimality for round 1 is not the most interesting challenge. I therefore encourage you to prepare your code for stochastic events such as:
- blocked cells forcing agents to reroute
- delayed departures causing agents to start later than expected
- unexecuted actions causing agents to replan their actions
We are still aiming for mid August release and will update you on more details about how stochastic events will be represented and with what probability they will occur.
We also encourage you to discuss among other participants how you solved the task and share your code publicly (there is also a community prize being awarded for contributions to the whole community).
If you have code that can be integrated into the FLATLAND to help everybody obtain better results don’t hesitate to contact us or open an issue on gitlab.
Best regards
Erik