cun-bjy / policy-distillation-baselines Goto Github PK
View Code? Open in Web Editor NEWPytorch Implementation of Policy Distillation for control, which has well-trained teachers via stable_baselines3.
License: GNU General Public License v3.0
Pytorch Implementation of Policy Distillation for control, which has well-trained teachers via stable_baselines3.
License: GNU General Public License v3.0
requirement.txt dosen‘t give the accurate versions of some python packages. Some packages are updated so that the project can't run. Could you please update the requirement.txt? Thank you!
Thank you for your work on single-task reinforcement and transfer learning!
But I think the most meaningful part of this paper is transfer learning on multi-task reinforcement learning.
If the teacher models are in different envs(for example, one is in CartPole-v1,others is in Acrobot-v1), the state space and action space of them are totally different. It is necessary to pay attention to how to design the input layer and output layer of the student model.
"About 90% of parameters are shared, with only 3 small MLP “controllers” on top which are task specific and allow for different action sets between different games." It's in the paper. But I don't know the details
Hello,
nice project =)
We would be also interested if you could do a pull request on stable-baselines3 where you add your project to the documentation (project section) ;)
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