In order to speed up the test time you can limit the number of trials per test (`nr_trials_per_test=10`). After you have made these changes to the file you can run `python score_tests.py` which will produce an output similiar to this:
```
Creating Test_0 with (x_dim,y_dim) = (10,10) and 1 Agents.
Running Test_0 with (x_dim,y_dim) = (10,10) and 1 Agents.
Progress: |********************| 100.0% Complete
Creating Test_1 with (x_dim,y_dim) = (10,10) and 3 Agents.
Progress: |********************| 100.0% Complete
Creating Test_2 with (x_dim,y_dim) = (10,10) and 5 Agents.
Progress: |********************| 100.0% Complete
Creating Test_3 with (x_dim,y_dim) = (50,10) and 10 Agents.
Progress: |********************| 100.0% Complete
Creating Test_4 with (x_dim,y_dim) = (20,50) and 10 Agents.
Progress: |********************| 100.0% Complete
Creating Test_5 with (x_dim,y_dim) = (20,20) and 15 Agents.
Test_0 score was -0.380 with 100.00% environments solved. Test took 0.62 Seconds to complete.
Running Test_1 with (x_dim,y_dim) = (10,10) and 3 Agents.
Progress: |********************| 100.0% Complete
Creating Test_6 with (x_dim,y_dim) = (50,50) and 10 Agents.
Test_1 score was -1.540 with 80.00% environments solved. Test took 2.67 Seconds to complete.
Running Test_2 with (x_dim,y_dim) = (10,10) and 5 Agents.
Progress: |********************| 100.0% Complete
Creating Test_7 with (x_dim,y_dim) = (50,50) and 40 Agents.
Progress: |********____________| 44.0% Complete
```
\ No newline at end of file
Test_2 score was -2.460 with 80.00% environments solved. Test took 4.48 Seconds to complete.
Running Test_3 with (x_dim,y_dim) = (50,10) and 10 Agents.
Progress: |**__________________| 10.0% Complete
```
The score is computed by
```
score = sum(mean(all_rewards))/max_steps
```
which is the sum over all time steps and the mean over all agents of the rewards. We normalize it by the maximum number of allowed steps for a level size. The max number of allowed steps is
```
max_steps = mult_factor * (env.height+env.width)
```
Where the `mult_factor` is a multiplication factor to allow for more time if difficulty is to high.
The number of solved envs is just the percentage of episodes that terminated with all agents done.
How these two numbers are used to define your final score will be posted on the [flatland page](https://www.aicrowd.com/organizers/sbb/challenges/flatland-challenge)
