Comments (4)
Hi, sorry for the late answer. Great, I'll be happy to review your work and assist with the implementation. A good start is with the tutorials and the example scripts. You can have a look at the implementation of ImitationLearning.
A new algorithm is added in the steps:
- A new policy, inheriting from BasePolicy or one of its subclasses
- A training script using low-level interfaces. See the existing examples
- Include the policy in the high-level Interfaces and prepare an example script
Step 3. can happen later, in a separate PR. I'm not very familiar with hierarchical imitation learning, but once you have a POC implementation, it will be a good basis for discussions. When the policy is finished, you can likely train it with the OfflineTrainer
from tianshou.
Hi. This is not on the current roadmap, but if you are interested in working on an implementation, I'm happy to discuss it with you.
Generally, the core team is currently more focused on improving interfaces and design than on including new algos. External contributions of new algos are welcome though!
from tianshou.
I would be interested on working on the implementation, I'll have to initially sketch out the tianshou repo, as I am not very familiar with it. It would be great if you could guide on the best way to implement the aforementioned algorithm in this framework :)
from tianshou.
You can discover all existing algorithms by looking at the implementations of BasePolicy
from tianshou.
Related Issues (20)
- how to run RL using multi-nodes in cluster HOT 1
- 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 2
- 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 4
- expected to be in range of [-1, 0], but got 1 HOT 3
- Unable to replicate original PPO performance HOT 7
- Clarification Needed on Implementing Action Masking in DQN with preprocess_fn in Collector
- will add dreamerv3 ?
- Documentation for multi-agent needs fixing
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from tianshou.