Hello,
Time for another update!
Since the last blog post, we have performed many new experiments with different encoders, decoders, recurrent versions, and flatter hierarchies. Out of these, the best new systems are:
- New encoder - single-byte weights for ESR (exponential sparse reconstruction) encoder using a few re-scaling tricks. Great for Arduino!
- New reinforcement learning decoder that performs conservative Q learning.
The latter in particular is quite nice to have. Previously, we used a type of ACLA algorithm (Actor-Critic Learning Automaton) to perform reinforcement learning. It worked well, but it had some downsides. For instance, the "passive learning" ... Read More →
Time for another update!
Since the last blog post, we have performed many new experiments with different encoders, decoders, recurrent versions, and flatter hierarchies. Out of these, the best new systems are:
- New encoder - single-byte weights for ESR (exponential sparse reconstruction) encoder using a few re-scaling tricks. Great for Arduino!
- New reinforcement learning decoder that performs conservative Q learning.
The latter in particular is quite nice to have. Previously, we used a type of ACLA algorithm (Actor-Critic Learning Automaton) to perform reinforcement learning. It worked well, but it had some downsides. For instance, the "passive learning" ... Read More →
Hello,
Time for another update!
We have added a new feature to the master branch of AOgmaNeo - the ability to supply "do not use" inputs.
These are supplied by just setting a column index to -1. They signal to the hierarchy that you do not want to learn or activate from the input. The hierarchy will however still provide a prediction for this column.
This new feature can be used for cases where data is missing or known to be useless, and also allows AOgmaNeo to predict what those "missing" values should be.
We have also added new serialization ... Read More →
Time for another update!
We have added a new feature to the master branch of AOgmaNeo - the ability to supply "do not use" inputs.
These are supplied by just setting a column index to -1. They signal to the hierarchy that you do not want to learn or activate from the input. The hierarchy will however still provide a prediction for this column.
This new feature can be used for cases where data is missing or known to be useless, and also allows AOgmaNeo to predict what those "missing" values should be.
We have also added new serialization ... Read More →
Here is our first update!
We have been working on some demos for AOgmaNeo. The two currently of most interest to us are:
- A new version of the "learning to walk faster" demo with a different (custom) quadruped robot with brushless motors (and of course the latest version of AOgmaNeo)
- A robotic rat that must solve classic rat maze tasks (made of cardboard). Here we are using a new version of our "smallest self-driving car" as the "rat"
These are progressing well. We are also of course experimenting with new things w.r.t. the AOgmaNeo software itself. For instance, ... Read More →
We have been working on some demos for AOgmaNeo. The two currently of most interest to us are:
- A new version of the "learning to walk faster" demo with a different (custom) quadruped robot with brushless motors (and of course the latest version of AOgmaNeo)
- A robotic rat that must solve classic rat maze tasks (made of cardboard). Here we are using a new version of our "smallest self-driving car" as the "rat"
These are progressing well. We are also of course experimenting with new things w.r.t. the AOgmaNeo software itself. For instance, ... Read More →