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重构Unity Wrapper #25

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

重构Unity Wrapper #25

StepNeverStop opened this issue Dec 28, 2020 · 0 comments
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enhancement New feature or request optimization Better performance or solution
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  1. 由python端在连接Unity时自动发送诸如“环境并行数量、智能体雷达检测密度、是否强制reset环境”等变量
  2. 由python端在初始化训练环境时指定是否需要stack状态输入，无需另写StackWapper
  3. ...
@StepNeverStop StepNeverStop created this issue from a note in Tasks (To Do) Dec 28, 2020
@StepNeverStop StepNeverStop self-assigned this Dec 28, 2020
@StepNeverStop StepNeverStop added the enhancement New feature or request label Dec 28, 2020
StepNeverStop added a commit that referenced this issue Dec 31, 2020
@StepNeverStop
refactor(unity): optimized unity training (#25)
080f381 
- add `initialize_config` in config.yaml
- add `reset_config` and `step_config` for unity training
- remove `GrayVisualWrapper`. `ResizeVisualWrapper`. `StackVisualWrapper`
@StepNeverStop StepNeverStop moved this from To Do to In Progress in Tasks Dec 31, 2020
StepNeverStop added a commit that referenced this issue Dec 31, 2020
@StepNeverStop
refactor(unity): using env_copys in yaml instead of --n_agents in…
efa6f65 
… command line when using unity training agents (#25)
StepNeverStop added a commit that referenced this issue Dec 31, 2020
@StepNeverStop
setting(unity): change default value of env_copys (#25)
b92ed95
StepNeverStop added a commit that referenced this issue Jan 1, 2021
@StepNeverStop
refactor(unity): optimize unity wrapper (#25)
747b3fa 
- add `UnitySingleBehaviorInfo` in indexs.py
- remove BasicActionWrapper
- remove redundant identifiers
StepNeverStop added a commit that referenced this issue Jan 5, 2021
@StepNeverStop
refactor(data): TAG 2.0.0 (#25, #33, open #31)
dc5391d 
- support multi-vector and multi-visual input
- optimize `gym` and `unity` wrapper
- fix `ActorCriticValueCts`
- tag 2.0.0
- add `ObsSpec`
- refactor `SingleAgentEnvArgs` and `MultiAgentEnvArgs`
- remove `self.s_dim`, use `self.concat_vector_dim` instead
- stop using vector input normalization temporarily
@StepNeverStop StepNeverStop added the optimization Better performance or solution label Jan 6, 2021
StepNeverStop added a commit that referenced this issue Jan 6, 2021
@StepNeverStop
style: remove redundant code (#25)
fb9e5eb
StepNeverStop added a commit that referenced this issue Jan 6, 2021
@StepNeverStop
fix&refactor(unity): optimize unity wrapper, fix ddpg (#25, #34)
7dad01d
StepNeverStop added a commit that referenced this issue Jan 6, 2021
@StepNeverStop
refactor(unity): optimize unity wrapper (#25)
719ccf4
StepNeverStop added a commit that referenced this issue Jul 2, 2021
@StepNeverStop
fix(multi-agents): implemented n-step replay buffer for multi-agents …
dbc6136 
…training. (#41,#25,#31)

1. change variable name from "is_lg_batch_size" to "can_sample"
2. optimized unity wrapper
3. optimized multi-agents replay buffers
StepNeverStop added a commit that referenced this issue Jul 4, 2021
@StepNeverStop
fix&feat: optimized training framework. (#41, #34, #33, #31, #25)
0a8d247 
1. fixed n-step replay buffer
2. reconstruct representation net
3. remove 'use_stack'
4. implement multi-agent algorithms with shared parameters
5. optimized agent network
StepNeverStop added a commit that referenced this issue Jul 28, 2021
@StepNeverStop
perf: optimized code-style. (#45, #41, #39, #35, #25)
a3c989a 
1. removed sarl off-policy algorithm pd_ddpg, 'cause it's not in main stream
2. updated README
3. removed `iql` and added script `IndependentMA.py` instead to implement independent multi-agent algorithms
4. optimized summary writing
5. move NamedDict from 'rls.common.config' to 'rls.common.specs'
6. updated example config
7. updated `.gitignore`
8. added property `is_multi` to identify whether training task is for sarl or marl for both unity and gym
9. reconstructed inheritance relationships between algorithms and their's superclass
10. removed `1.e+18` in yaml files and use a large integer number instead, 'cause we want a large integer rather than float
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 Jul 29, 2021
@StepNeverStop
v4.1.2 fix(unity): fixed bugs in continuous training and unity traini…
f95a510 
…ng. (#34, #25)

1. updated `setup.py`
2. removed redundant packages
3. fixed bugs in unity wrapper
4. fixed bugs in agent models that occurred in continuous-action training tasks
5. fixed bugs in class `MLP`
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 27, 2021
@StepNeverStop
fix(unity): fixed visual input training using Unity3D (#34, #25)
39d60c3
StepNeverStop added a commit that referenced this issue Sep 4, 2021
@StepNeverStop
v0.0.1 perf(env): optimized env wrapper and data storing. (#31,#34,#25)
7d5e00f 
*. redefine version to v0.0.1
1. removed package `supersuit`
2. implemented class `MPIEnv`
3. implemented class `VECEnv`
4. optimized env wrappers, implemented `render` method to `gyms` environment.
5. reconstructed some of returns of `env.step` from `obs` to `obs_fa` and `obs_fs`.
  - `obs_fa` is used to choose action based by agent/policy. For the cross point of episode i and i+1, `obs_fa` represents $observation_{i+1}^{0}$, otherwise it keeps same with `obs_fs` which represents $observation_{i}^{t}$.
  - `obs_fs` is used to be stored in buffer. For the cross point of episode i and i+1, `obs_fs` represents $observation_{i}^{T}$, otherwise it keeps same with `obs_fa`.
6. optimzed `rssm` related based on mentioned `obs_fs`.
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