This is the first commit of 6 commits mentioned in #274, which features
1. Refactor of `Class Net` to support any form of MLP.
2. Enable type check in utils.network.
3. Relative change in docs/test/examples.
4. Move atari-related network to examples/atari/atari_network.py
Co-authored-by: Trinkle23897 <trinkle23897@gmail.com>
This is the PR for C51algorithm: https://arxiv.org/abs/1707.06887
1. add C51 policy in tianshou/policy/modelfree/c51.py.
2. add C51 net in tianshou/utils/net/discrete.py.
3. add C51 atari example in examples/atari/atari_c51.py.
4. add C51 statement in tianshou/policy/__init__.py.
5. add C51 test in test/discrete/test_c51.py.
6. add C51 atari results in examples/atari/results/c51/.
By running "python3 atari_c51.py --task "PongNoFrameskip-v4" --batch-size 64", get best_result': '20.50 ± 0.50', in epoch 9.
By running "python3 atari_c51.py --task "BreakoutNoFrameskip-v4" --n-step 1 --epoch 40", get best_reward: 407.400000 ± 31.155096 in epoch 39.
Cherry-pick from #200
- update the function signature
- format code-style
- move _compile into separate functions
- fix a bug in to_torch and to_numpy (Batch)
- remove None in action_range
In short, the code-format only contains function-signature style and `'` -> `"`. (pick up from [black](https://github.com/psf/black))
1. add policy.eval() in all test scripts' "watch performance"
2. remove dict return support for collector preprocess_fn
3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)`
4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184)
5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard
6. add test_returns (both GAE and nstep)
7. change the type-checking order in batch.py and converter.py in order to meet the most often case first
8. fix shape inconsistency for torch.Tensor in replay buffer
9. remove `**kwargs` in ReplayBuffer
10. remove default value in batch.split() and add merge_last argument (#185)
11. improve nstep efficiency
12. add max_batchsize in onpolicy algorithms
13. potential bugfix for subproc.wait
14. fix RecurrentActorProb
15. improve the code-coverage (from 90% to 95%) and remove the dead code
16. fix some incorrect type annotation
The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
* remove dummy net; delete two files
* split code to have backbone and head
* rename class
* change torch.float to torch.float32
* use flatten(1) instead of view(batch, -1)
* remove dummy net in docs
* bugfix for rnn
* fix cuda error
* minor fix of docs
* do not change the example code in dqn tutorial, since it is for demonstration
Co-authored-by: Trinkle23897 <463003665@qq.com>
