Comments (2)
Hi @xugami -- which version of pymdp
are you using? It seems like you might be using v0.0.3 or v0.0.4.
This error has been fixed in the latest release (see here).
If my suspicion is correct, then you should be able to fix this by upgrading your version of pymdp
:
pip install --upgrade inferactively-pymdp
Alternatively, you can install the dev verison of pymdp
, instructions for which can be found here.
from pymdp.
Thanks , this problem has been fixed by upgrading from v0.0.4 to v0.0.5.
from pymdp.
Related Issues (20)
- Habit learning and precision HOT 5
- Allow learning of likelihood and transition likelihood parameters with sequences of hidden state posteriors
- Sparse representation of likelihood tensors (`A` and `B` arrays) HOT 2
- ! pip install inferactively-pymdp HOT 3
- Choose random action if all action probabilities are the same, even if sampling is deterministic (i.e., some tolerance of probability) HOT 2
- Fix assertion when whether `num_controls` is consistent with actions enumerated in policy space
- Remove redundant operation for normalization in case of 3-tensors
- Incorrect calculation of cross-entropy in the calc_free_energy function. HOT 1
- an error in the official documentation HOT 1
- Incorrect error message when ensuring normalization of D array/vectors
- Allow `alpha` (action precision) to be passed into `Agent()` constructor as optional argument HOT 1
- State inference: Considerable performance difference with MATLAB implementation? HOT 4
- Better error messages for MMP
- Bug when `num_controls` is passed as an argument to `Agent` constructor HOT 1
- Error when using `stochastic` mode action sampling in case that `num_controls[f] = 1` HOT 1
- Allow direct sampling / selection from posterior over policies, rather than only from posterior over actions HOT 1
- Move plot functions from explicit definition in demos to `utils` module, change axes tick labels calls
- einsum while calculating free energy HOT 4
- Sampling action when using deep policy HOT 2
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