Questions tagged [batch-learning]

For questions about machine learning algorithms that learn in batches of data rather than one example at a time (i.e. online learning). Batch learning can also be called offline learning and it is the common way of training machine learning models.

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Are batches useful for REINFORCE without strong episode cutoffs?

I'm following along with PyTorch's example implementations (found here) of reinforcement learning algorithms that happen to be largely REINFORCE (vanilla policy gradient) based, and I notice they don'...
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0 votes
0 answers
9 views

overcoming the original policy with offline RL

I am doing a comparison between RL and a metaheuristic algorithm. What I have found is that online RL does a good work but does not overcome the latter algorithm. In this case generating the samples ...
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0 votes
0 answers
43 views

How is it possible to use batches of data from within the same sequence with an LSTM?

ETA: More concise wording: Why do some implementations use batches of data taken from within the same sequence? Does this not make the cell state useless? Using the example of an LSTM, it has a hidden ...
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2 votes
1 answer
73 views

How to sample the tuples during the initial time steps of the DDPG algorithm?

I am facing an issue in understanding the following line from the pseudocode of the DDPG algorithm Sample a random minibatch of $N$ transitions $(s_i, a_i, r_i, s_{i+1})$ from $R$ Here $N$ is a ...
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0 votes
1 answer
39 views

Having the negative cases in the same batch vs. shuffling the dataset

I am working on a model for an NLP task. The model encodes the text and has a regression output layer. In this task, from each instance (positive), I create several negative cases using a specific ...
  • 123
0 votes
1 answer
153 views

Action selection in Batch-Constrained Deep Q-learning (BCQ)

For simplicity, let's consider the discrete version of BCQ where the paper and the code are available. In the line 5 of Algorithm 1 we have the following: $$ a' = \text{argmax}_{a'|G_{\omega}(a', s')/\...
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2 votes
0 answers
27 views

Methodologies for passing the best samples for a neural network to learn

Just an idea I am sure I read in a book some time ago, but I can't remember the name. Given a very large dataset and a neural network (or anything that can learn via something like stochastic gradient ...
0 votes
1 answer
89 views

Is it okay to calculate the validation loss over batches instead of the whole validation set for speed purposes?

I have about 2000 items in my validation set, would it be reasonable to calculate the loss/error after each epoch on just a subset instead of the whole set, if calculating the whole dataset is very ...
2 votes
1 answer
392 views

Offline/Batch Reinforcement Learning: when to stop training and what agent to select

Context: My team and I are working on a RL problem for a specific application. We have data collected from user interactions (states, actions, rewards, etc.). It is too costly for us to emulate agents....
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3 votes
0 answers
784 views

Why would a VAE train much better with batch sizes closer to 1 over batch size of 100+?

I've been training a VAE to reconstruct human names and when I train it on a batch size of 100+ after about 5 hours of training it tends to just output the same thing regardless of the input and I'm ...
2 votes
1 answer
95 views

Is batch learning with gradient descent equivalent to "rehearsal" in incremental learning?

I am learning about incremental learning and read that rehearsal learning is retraining with old data. In essence, isn't this the exact same thing as batch learning (with stochastic gradient descent)? ...
1 vote
1 answer
435 views

Why does the output shape of a Dense layer contain a batch size?

I understand that the batch size is the number of examples you pass into the neural network (NN). If the batch size is 10, it means you feed the NN 10 examples at once. Assuming I have an NN with a ...
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1 vote
1 answer
241 views

What is the difference between batches in deep Q learning and supervised learning?

How is the batch loss calculated in both DQNs and simple classifiers? From what I understood, in a classifier, a common method is that you sample a mini-batch, calculate the loss for every example, ...
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12 votes
1 answer
5k views

What is the relation between online (or offline) learning and on-policy (or off-policy) algorithms?

In the context of RL, there is the notion of on-policy and off-policy algorithms. I understand the difference between on-policy and off-policy algorithms. Moreover, in RL, there's also the notion of ...
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