reinforcement-learning
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../build/vowpalwabbit/vw --marginal f --noconstant --initial_numerator 0.5 --initial_denominator 1.0 --decay 0.001 --readable_model readable_model.txt
Data:
0.5 |m constant id1
1.0 |m constant id2
0.25 |m constant id3
0.4 |m constant id1
Observed invert hash model:
Version 8.11.0
Id
Min label:0
Max label:1
bits:18
lda:0
0 ngram:
0 skip:
options: --marginal m
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Bidirectional RNN
Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?
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The following applies to DDPG and TD3, and possibly other models. The following libraries were installed in a virtual environment:
numpy==1.16.4
stable-baselines==2.10.0
gym==0.14.0
tensorflow==1.14.0
Episode rewards do not seem to be updated in model.learn() before callback.on_step(). Depending on which callback.locals variable is used, this means that:
- episode rewards may n
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When users starts Serve cluster with a set of options (http options, checkpoint path) and then connects to it with a different set of options, we should either update it, or error out.