Questions tagged [observation-spaces]

For questions about observation spaces in the context of reinforcement learning and other AI sub-fields.

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Should I apply normalization to the observations in deep reinforcement learning?

I am new to DRL and trying to implement my custom environment. I want to know if normalization and regularization techniques are as important in RL as in Deep Learning. In my custom environment, the ...
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33 views

How do neural networks deal with inputs of different sizes that are padded in order to have them of the same size?

I am trying to create an environment for RL where the size of my input (observation space) is not fixed. As a way around it, I thought about padding the size to a maximum value and then assigning &...
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15 views

Scrabble-MuZero: combine observation planes of different shape

I'm working on an implementation of Scrabble with MuZero. The board state is represented by a matrix with shape $15 \times15 \times 27$ ($26$ letters $+ 1$ wildcard, value $0/1$) and the rack state $...
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24 views

Scrabble rack observation with MuZero

Currently I'm trying to implement Scrabble with MuZero. The $15 \times 15$ game board observation (as input) is of size $27 \times15 \times15$ (26 letters + 1 wildcard) with a value of 0 or 1. However ...
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7 views

What is the rationale behind the minimap of MAgent?

The MAgent family of PettingZoo is based on a previous implementation that gives a little tutorial explaining the gridworld ...
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2answers
271 views

What happens when the agent faces a state that never before encountered?

I have a network with nodes and links, each of them with a certain amount of resources (that can take discrete values) at the initial state. At random time steps, a service is generated, and, based on ...
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74 views

Does the order in which the features are concatenated to create the state (or observation) matter?

I'm experimenting with an RL agent that interacts with the following environment. The learning algorithm is double DQN. The neural network represents the function from state to action. It's build with ...