pip install -e gym-duckietown
We will evaluate your system on 5 maps together with start and goal locations to compute the average accumulated reward. A primary component of your grade is how fast the robot finds the goal, and the reward achieved. In particular, take note below:
- Oscillating in place may generate a high score in the simulator, but will be penalized in our evaluation. This is a flaw in the Duckietown simulator.
To initialize the gym-duckietown environment, there are five arguments:
--map-name: the name of the map--seed: random seed of the environment--start-tile: the starting tile. E.g. 1,13--goal-tile: the goal tile. E.g. 3,3--max-steps: the maximum run step. The default value is 1500. Do not change this default value when you generate the control files for submission.
Generate the control files for submission, similar to what you have done in Lab 1. A sample file for the python environment is available here together with sample control file. They illustrate how to add arguments and output your controls to a file. To try our simple policy,
python example.py --map-name map4_0 --seed 2 --start-tile 1,13 --goal-tile 3,3
The robot is only allowed to take in the following information as input:
- the starting tile
- the goal tile
- a map image
- the goal images
- image observation after each step
- rough robot position after each step
More details can be found in example.py.