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优化ReadMe中的get start #26

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StepNeverStop opened this issue Dec 28, 2020 · 0 comments
Open

优化ReadMe中的get start #26

StepNeverStop opened this issue Dec 28, 2020 · 0 comments
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docs Additional explanations are required enhancement New feature or request

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  1. ReadMe中关于如何使用该项目介绍的不够详细，需要进一步阐述和举例说明；
  2. 示例阐述如何基于本仓库构建自己新的算法
@StepNeverStop StepNeverStop self-assigned this Dec 28, 2020
@StepNeverStop StepNeverStop added enhancement New feature or request docs Additional explanations are required labels Dec 28, 2020
StepNeverStop added a commit that referenced this issue Jul 13, 2021
@StepNeverStop
perf: optimized commands. (#26)
ae41767
StepNeverStop added a commit that referenced this issue Jul 29, 2021
@StepNeverStop
v4.1.0 perf: implemented unified env interface.(#34, #31, #26, #25, #40)
941e14d 
1. added `test.yaml` for quickly verify RLs
2. change folder name from `algos` to `algorithms` for better reading
3. removed single agent recoder, all algorithms(sarl&marl) using  `SimpleMovingAverageRecoder`
4. removed `GymVectorizedType` in `common/specs.py`
5. removed `common/train/*`, and implement unified training interface in `rls/train`
6. reconstructed `make_env` function in `rls/envs/make_env`
7. optimized function `load_config`
8. moved `off_policy_buffer.yaml` to `rls/configs/buffer`
9. removed configurations like `eval_while_train`, `add_noise2buffer` etc.
10. optimized environments' configuration files
11. optimized environment wrappers and implemented unified env interface for `gym` and `unity`, see `env_base.py`
12. updated dockerfiles
13. updated README
StepNeverStop added a commit that referenced this issue Aug 25, 2021
@StepNeverStop
perf: reconstruct repo(#47, #25, #46, #34, #31, #33, #39, #41, #45, #26)
67b8979
StepNeverStop added a commit that referenced this issue Aug 31, 2021
@StepNeverStop
v5.1.7 perf: optimized codebase (#34, #28, #26)
abf6b0a 
1. fixed rnn hidden states iteration
2. renamed `n_time_step` to `chunk_length`
2. added `train_interval` to both sarl and marl off-policy agorithms so as to control the training frequency related to data collecting
3. added `n_step_value` to calculate n-step return
4. updated README
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