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Hello,
1. The current implementation for matrix multiplication uses BRGEMM algorithm. Is there any implementation of "Low Rank Approximation approach" for matrix multiplication in oneDNN? Is there a…
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# 🌟 New SSL approach addition
## Approach description
NNCLR: https://arxiv.org/abs/2104.14548
> Self-supervised learning algorithms based on instance discrimination train encoders to be invar…
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In `test_on_GAS.ipynb`, the labels `spn` and `fspn` are switched.
`plt.plot(x, card[idx[700:800]], color="red", alpha=0.5, label = "spn")`
`plt.plot(x, card2[idx[700:800]], color="blue", alpha=0.5…
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On running the lagrangian version of SAC I get the following curve for costs. I tried changing the constraint limits to a range of values and didn't get much benefit:
 [11] is an adaptation …
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I have several questions:
1- When I compared with algorithm presented in"Human-level control through deep reinforcement learning", I can not find the third initialization (initial target action value…
fi000 updated
3 years ago
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http://www.ias.tu-darmstadt.de/uploads/Publications/Kober_IJRR_2013.pdf
https://en.wikipedia.org/wiki/State-Action-Reward-State-Action
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[https://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html](url)
https://scikit-learn.org/dev/tutorial/machine_learning_map/index.html#ml-map
![image](https://use…
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## **Results**
Your agent always plays first so it's supposed to win/tie against a random players, a good amount of time it loses, so there is clearly something wrong with the Q learning algorithm.
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