Comments (3)
The buffers are handled through the trainer, not the policy. Currently the buffer interfaces are tied rather strictly to the policy interfaces, but we might relax them in the (not very close) future.
I suggest you work your way through the trainer's methods to understand the dataflow of tianshou :)
from tianshou.
I think you should create your own Buffer class inherited from ReplayBuffer
, and using ReplayBufferManager
to combine all buffers
from tianshou.
Thank you, it sounds like this can be achieved, let me try first
from tianshou.
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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