Comments (6)
From my previous experience, (in Q-learning based algorithms) if using an eps in the training phase but zero eps in the test phase, the performance would drop down; but with smaller eps but greater than zero, it works well.
Sorry for the late reply. I'm busy with my graduation these days (and Tsinghua U is in Beijing :(
from tianshou.
From my previous experience, (in Q-learning based algorithms) if using an eps in the training phase but zero eps in the test phase, the performance would drop down; but with smaller eps but greater than zero, it works well.
Sorry for the late reply. I'm busy with my graduation these days (and Tsinghua U is in Beijing :(
Oh, poor you.
Maybe it should leave an interface to users, so that they could decide whether to add an action noise in evaluation?
Hope you guys are fine.
from tianshou.
Yes, I agree with you. Maybe you can submit another pull request once you come up with the interface design.
from tianshou.
Sorry for the late replay. I've submitted a pull request with the interface design.
from tianshou.
Is it necessary to decrease noise as the training process goes on (in ddpg policy)? Can I do this in tianshou?
在ddpg中有必要将噪声衰减吗?在天授框架中可以这样做吗?
from tianshou.
Is it necessary to decrease noise as the training process goes on (in ddpg policy)? Can I do this in tianshou?
As you wish. Just modify the Gaussian Noise class and add a counter, each __call__
will increase the counter and decrease sigma a little.
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Related Issues (20)
- Revisit `Launcher` for starting multiple experiments HOT 1
- Adjust locations of setting the policy in train/eval mode HOT 1
- Change log is chaotic and partly uninformative HOT 2
- how to run RL using multi-nodes in cluster HOT 2
- Potential confusion about where start timesteps are collected in HL interfaces HOT 4
- Use Altair inside a notebook to display benchmark results
- Does Tianshou truly supports MARL out of the box? HOT 1
- Extend benchmark with mujoco v4 envs
- How can I make action sampling within the range specified by my environment when using onpolicy_trainer? HOT 6
- Document effects of the relations between buffer size, num workers and episode length
- Poetry update the torch versioned from cuda (2.0.1+cu118) to cpu (2.1.1) defaultly on Windows HOT 9
- [question] Why does Tianshou use a replay buffer in on-policy RL algorithms? HOT 1
- ImportError: cannot import name 'Self' from 'typing' (/root/miniconda3/lib/python3.10/typing.py) HOT 1
- ModuleNotFoundError: No module named 'tianshou.highlevel' HOT 2
- Support dict observation spaces in highlevel api
- get_env_attr not working in SubprocVectorEnv? HOT 4
- How to save the log which axis is each epoch not epoch's steps? HOT 2
- Python Bug: lambda function refers only one environment HOT 5
- expected to be in range of [-1, 0], but got 1 HOT 4
- Unable to replicate original PPO performance HOT 21
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