Comments (2)
It should give you rewards at every time step (at a typical gym-interface fashion).
Do note that this is a (somewhat) sparse-reward environment. Non-zero rewards are given only upon successfully delivering a requested shelf.
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It should give you rewards at every time step (at a typical gym-interface fashion).
Do note that this is a (somewhat) sparse-reward environment. Non-zero rewards are given only upon successfully delivering a requested shelf.
Thanks for your reply!Another question that is when I run the test_obs_space_2 in test_env.py,I meet this error"assert len(gym.spaces.flatten(s, o)) == env._obs_length E assert 133 == 116".What should I do?Hope for your soon reply!
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Related Issues (19)
- Import error HOT 7
- gym.make gives an error HOT 3
- Applying Reinforcement Learning HOT 2
- gym.make("rware:rware-tiny-2ag-v1") assertion error HOT 2
- Cannot run the basic program HOT 2
- An error when using render(). HOT 2
- Question about the observation space HOT 1
- AttributeError: `Warehouse` object has no attribute `shelfs` HOT 4
- Can I unload at a shelf instead of the Goal location? HOT 4
- modify the environment HOT 1
- Check for whether agent is unloading shelf at a location where another shelf is already present. HOT 2
- Observation Space Interpretation HOT 7
- PettingZoo Compatibility HOT 1
- Gym 0.20.0 breaks observation spaces and environment HOT 1
- I got this error HOT 1
- Environment breaks down when an agent delivers a shelf HOT 3
- Bugs caused by gym version changes HOT 4
- creating the environment HOT 1
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