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This issue appeared after updating from 13.0.1 to 14.0.
I am sure, that `clangd` is able to find system includes using extracted paths from `arm-none-eabi-*` GCC, because linting of `test.cpp` files …
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## Describe the bug
When running mujoco env, with some configuration it raises this warning.
## To Reproduce
On my laptop
```bash
$ cd benchmark
$ python3 test_envpool.py --env mujoco --…
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https://github.com/openai/gym/tree/master/gym/envs/mujoco
Env List:
- [x] Ant-v4 (#74)
- [x] HalfCheetah-v4 (#75)
- [x] Hopper-v4 (#76)
- [x] Humanoid-v4 (#77)
- [x] HumanoidStandup-v4 (#78)…
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https://github.com/deepmind/acme
Road Map:
@TianyiSun316
- [ ] Go through ACME codebase and integrate vector_env to the available algorithms;
- [ ] Write Atari examples;
- [ ] Check Atari pe…
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This is a request for the fairly distance future, but ideally the ALE would be able to natively run on CUDA/TPUs/etc. in addition to CPUs in the same vein as brax. https://github.com/NVlabs/cule alrea…
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## Describe the bug
PPO can no longer reproduce 400 game scores in the `Breakout-v5` given 10M steps of training (same hyperparameters) as it can in `BreakoutNoFrameskip-v4`.
![image](https://u…
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- [ ] I have marked all applicable categories:
+ [ ] exception-raising bug
+ [x] RL algorithm bug
+ [ ] documentation request (i.e. "X is missing from the documentation.")
+ [ ] ne…
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I found the cpu usage can be as high as 1200% for each process when using parallel simulation. How to restrict the cpu usage? Thanks.
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## Motivation
Related to #33
I was trying to make env pool work with SB3 and I noticed different inconsistencies with classic gym envs / gym vector envs.
I wrote a wrapper but currently there …
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The -v5 Gym Atari environments have sticky actions enabled by default (with `repeat_action_probability=0.25`, see [here](https://github.com/mgbellemare/Arcade-Learning-Environment#openai-gym)). This m…