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ReplayBuffer storing actions size mismatch during env reset #278
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Hi @defrag-bambino, Which action space are you using? Are they continuous actions? @belerico might it make sense to have a wrapper that flattens the continuous actions? |
Yes, it is a continuous "Box" Space. |
I've tried to work around it using np.squeeze() and np.expand_dims() in relevant places of my env wrapper. This seems to work for now. StacktraceTraceback (most recent call last): Seems like the same holds for the observation shape |
Yep, we can add it and leave it to the user to use it |
If your observation space is a 1D vector, then you should also remove the leadning 1 in the dimension i suppose. Can you try it? |
Hi @defrag-bambino, we're sorry but right now Multi-Agent RL (MARL) is not supported, so your actions and observations space must be unrelated from the number of agents, which are considered as independentfrom one another. This means that:
|
Maybe there could be a solution as explained in #241 |
Hi,
I am trying to write a simple gym wrapper for an existing env.
During testing, I am not facing the following issue:
Which, I think, originates from this line:
reset_data["actions"] = np.zeros((1, reset_envs, np.sum(actions_dim)))
(line 643 in dreamer_v3.py). My env hasaction_space.shape
of (1,4) - but in this line it is summing up to1+4=5
.Is this the desired behavior?
Thanks
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