How to add new maps for RL agent to learn? about challenge-aido_rl-il HOT 9 OPEN

You need to specify name of the maps without the .yamlextension like the following:
map_names=["zigzag_dists", "4way", "loop_empty", "small_loop"] as shown in /tutorials/get_tile_coordinates.py

If you would like to collect tile coordinates for all the maps, you can do so by:

import os 
map_names = os.listdir("maps")

and running the script with:
python get_tile_coordinates.py will generate the file /challenge-aido_RL-IL/tutorials/tile_coordinates.csv where all the tile coordinates for each of the map are stored.

Remember that you need to copy the coordinates information (whatever inside the .csv file) and put it accordingly inside get_tiles function in duckietown_rl/gym_duckietown/simulator.py, as described here.

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Thanks for your reply! Yes, I tried this. But I don't know why my new map will have this error.
Here is my new map:

I want to set an obstacle in the middle of the road.

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Oh, I see. I haven't created a map on my own, so please check out the official repository of gym_duckietown, especially the Design part, where they explain the structure of .yaml file. They say the array of tiles should be 2-d array, as far as I can see your array is 1-d. Perhaps, that causes an issue

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You need to specify name of the maps without the .yamlextension like the following:
map_names=["zigzag_dists", "4way", "loop_empty", "small_loop"] as shown in /tutorials/get_tile_coordinates.py

If you would like to collect tile coordinates for all the maps, you can do so by:

import os 
map_names = os.listdir("maps")

and running the script with:
python get_tile_coordinates.py will generate the file /challenge-aido_RL-IL/tutorials/tile_coordinates.csv where all the tile coordinates for each of the map are stored.

Remember that you need to copy the coordinates information (whatever inside the .csv file) and put it accordingly inside get_tiles function in duckietown_rl/gym_duckietown/simulator.py, as described here.

Thanks for your reply! I follow the above instruction and put the map "loop_obstacles" into the simulator, but it occurs this error when I run python -m scripts.train_ddpg.

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I edited the simulator such that every time a map is loaded/reset, the agent starts at a 'good' position. I improve the 'goodness' of the starting point with 2 things:

1. by default simulator chooses a point randomly in a tile to launch the agent on that point. One thing I do is that I narrow down the interval so that the agent does not start at the edges of the tiles (the disadvantage of starting at the edge is that RL agent might go off the tile quickly).

2. I want the robot to start nearby the centre of the tile (not further away than a threshold I defined: 0.18 meters) AND the angle that it needs to turn to align itself with the centre line of the tile should not be larger than 100 degrees, again a threshold defined by me.

Simulator tries to match all of the conditions above for a fixed number of times (MAX_SPAWN_ATTEMPTS, which is by default 5000 as you see in the error). So what happened with your code is that the simulator could not find a proper point in the map which satisfies all the conditions above. And after 5000 steps, it stopped.

See the 2 simulator.py files to see the differences: my code - original code. You might stretch out the constraints and perhaps the simulator will find a 'good' spot to launch the agent

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One very weird thing is that i can run the "loop obstacles" map successfully with python PIDcontroller.py but it cannot run in python -m scripts.train_ddpg, but it is the same map configuration.

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Yes, that looks strange.
Are you sure the file tutorials/maps/loop_obstacles.yaml is exactly the same as in duckietown_rl/maps/loop_obstacles.yaml ?

If yes, then I would guess its luck! Cause remember the simulator selects a point to launch the agent randomly. Even though it's a low possibility, it might happen that in one run, simulator finds a suitable point in 5k trials

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I edited the simulator such that every time a map is loaded/reset, the agent starts at a 'good' position. I improve the 'goodness' of the starting point with 2 things:

1. by default simulator chooses a point randomly in a tile to launch the agent on that point. One thing I do is that I narrow down the interval so that the agent does not start at the edges of the tiles (the disadvantage of starting at the edge is that RL agent might go off the tile quickly).
2. I want the robot to start nearby the center of the tile (not further away than a threshold I defined: 0.18 meters) AND the angle that it needs to turn to align itself with the center line of the tile should not be larger than 100 degrees, again a threshold defined by me.

Simulator tries to match all of the conditions above for a fixed number of times (MAX_SPAWN_ATTEMPTS, which is by default 5000 as you see in the error). So what happened with your code is that the simulator could not find a proper point in the map which satisfies all the conditions above. And after 5000 steps, it stopped.

See the 2 simulator.py files to see the differences: my code - original code. You might stretch out the constraints and perhaps the simulator will find a 'good' spot to launch the agent

Thanks,😁 I tried to change the x,z initial position as +1 rather than +0.8. I found that it is easy to start a good pose now.

One thing that I don't understand is whether the robot can learn to avoid the obstacles in your code? For example in ddpg scripts, I haven't seen where we could get the other objects' information.

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Avoiding obstacles was not in my project's goals, so I did not build an algorithm for that.

One might do the following:

• Use the function proximity_penalty2 (See link) to determine whether the agent collided or not
• Edit reward function: subtract reward, in other words, add a penalty, if the agent collides with static/dynamic objects (See an example)

Try out the reward function if it behaves in the way you would want to: e.g. does the reward is less when the agent collides, etc. Then, you can train the RL agent and hopefully it will learn to avoid obstacles

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