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Fix Reacher-v5 & Pusher-v5 reward function being calculated using previous state #832

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Merged
pseudo-rnd-thoughts merged 6 commits into from
Dec 11, 2023
Merged

Fix Reacher-v5 & Pusher-v5 reward function being calculated using previous state #832

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d32bde9
Update reacher_v5.py
Kallinteris-Andreas Dec 9, 2023
3b0e7da
typo
Kallinteris-Andreas Dec 9, 2023
43e8b07
Update reacher_v5.py
Kallinteris-Andreas Dec 9, 2023
b1f7fb4
Update pusher_v5.py
Kallinteris-Andreas Dec 9, 2023
5ac1bb2
Update pusher_v5.py
Kallinteris-Andreas Dec 9, 2023
75ec427
Update test_mujoco_v5.test_identical_behaviour_v45()
Kallinteris-Andreas Dec 9, 2023
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Update reacher_v5.py
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@Kallinteris-Andreas
Kallinteris-Andreas authored Dec 9, 2023
commit 43e8b0711b6feb2e4efcd816bcd6ac99da2a3a62
2 changes: 1 addition & 1 deletion gymnasium/envs/mujoco/reacher_v5.py
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Expand Up @@ -133,9 +133,9 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
## Version History
* v5:
- Minimum `mujoco` version is now 2.3.3.
- Fixed bug: `reward_distance` was based on the state before the physics step, now it is based on the state after the physics step (related [Github issue](https://github.com/Farama-Foundation/Gymnasium/issues/821)).
- Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments.
- Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages.
- Fixed bug: `reward_distance` was based on the state before the physics step, now it is based on the state after the physics step (related [Github issue](https://github.com/Farama-Foundation/Gymnasium/issues/821)).
- Removed `"z - position_fingertip"` from the observation space since it is always 0, and therefore provides no useful information to the agent, this should result is slightly faster training (related [Github issue](https://github.com/Farama-Foundation/Gymnasium/issues/204)).
- Added `xml_file` argument.
- Added `reward_dist_weight`, `reward_control_weight` arguments, to configure the reward function (defaults are effectively the same as in `v4`).
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