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Using multiple sensor modalities to improve exploration for robotic manipulation tasks with sparse rewards
The exploration makes the training worse
I'm getting the following error in MuJoCo:
raise MujocoException('Got MuJoCo Warning: {}'.format(warn)) mujoco_py.builder.MujocoException: Got MuJoCo Warning: Nan, Inf or huge value in QACC at DOF 0. The simulation is unstable. Time = 0.4000.
Based on this, this, this, and this the error is due to time-stepping or actions that are too large.
There are two things that I noticed:
First install the repo. Then, to reproduce the bug starting from the very beginning of training run
python main.py
To reproduce the bug starting at a later point in the training run
python main.py --debug
Is my cpu the problem?
Do I need to normalize the observations?
Hi, jmichaux!
I'm interested in your project and I've installed all the libraries you mentioned. I got the following error when I run the main.py:
ModuleNotFoundError: No module named 'multimodal_envs'
I can't find any helpful information and any help will be grateful. Thanks.
Right now intrinsic rewards are given on any transition. Intuitively, this isn't quite right because we end up rewarding the agent when it dies. This could, in theory, lead to excessive exploration a la the Noisy TV problem. In practice it doesn't really seem to matter. But it would be interesting to see if we can speed up learning by only giving the exploration bonus on transition where the agent doesn't fail.
Right now I am only parallelizing the environments to collect more data using the same agent. Does it make sense to use multiple agents for updating the weights? How would I do this? MPI?
im trying to do research and beat SOTA for a school project, and I want to know if this is based off of a paper that I can somehow improve upon?
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