Comments (4)
@alexdavey feel free to make a PR
from gymnasium-robotics.
Your test for sparse rewards, is not correct, since using random actions is very unlikely to get you to the terminal state
Does this also affect the PointEnv
?
from gymnasium-robotics.
Thanks for taking a look at this issue.
- I agree that it is not a robust test in general, it’s primarily just a check to compare pre/post commit (after the suggested changes, it does get a very small but non-zero reward). If you have suggestions for a more robust test I can implement that, but I think the issue is fairly clear from the step method of AntMaze:
Gymnasium-Robotics/gymnasium_robotics/envs/maze/ant_maze.py
Lines 277 to 289 in d0a10dc
and comparing to the PointMaze step:
Gymnasium-Robotics/gymnasium_robotics/envs/maze/point_maze.py
Lines 379 to 391 in d0a10dc
.compute_terminated()
is common to both and resets the goal location if within 0.45.
This issue previously existed for PointMaze. I’m proposing to make the same fix as commit ace181e, but for AntMaze.
- The PointEnv/PointMaze env’s are not affected by either of these issues, due to the commit referenced above, and because
maze_size_scaling = 1
so the missing factor does not change anything.
from gymnasium-robotics.
- I would say it is worrying that
compute_terminated
is not a pure function, it should be refactored into 2 functions:compute_terminated
(but now it just computes if it is terminated), andupdate_goal
These changes would requite a new revision ("AntMaze_UMaze-v4")
from gymnasium-robotics.
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