Questions tagged [continuous-state-spaces]

For questions about continuous state spaces, in the context of reinforcement learning or other AI sub-fields.

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Algorithms for average reward reinforcement learning in continuous/general state-action space

I see that discounted reward reinforcement learning has been extensively studied in the literature. However, the average reward metric receives less attention, and it looks like algorithms for this ...
k2pctdn's user avatar
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Variable observation space at each episode

I have an enviroment with continuous actions and state variables. Every time I reset my env, between 2 and 5 balls spawn randomly in a box of 100x100 size. One of those balls (the red one) will ...
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RL - Can RL be applied to problems where the next state is not the next observation?

I'm quite new on the study of reinforcement learning, and Im working on a communication problem with continuous large actions range for my final graduation work. I'm trying to use Gaussian Policy and ...
MaarcosNascimen's user avatar
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Is there a gentle introduction to reinforcement learning applied to MDPs with continuous state spaces?

I am looking for a gentle introduction (videos, lecture notes, tutorials, books) on reinforcement learning (MDPs) involving continuous states (or very large cardinality of state space). In particular, ...
cgo's user avatar
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How to generalize finite MDP to general MDP?

Suppose, for simplicity sake, to be in a discrete time domain with the action set being the same for all states $S \in \mathcal{S}$. Thus, in a finite Markov Decision Process, the sets $\mathcal{A}$, $...
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Predicting next 2D location from sparse 2D inputs which are received sequentially

Problem: You toss a coin on a 2D table with known dimension. There are certain regions on the table where the probability of get heads is high. At the maximum you can toss N=20 times at an arbitrary ...
goldfinch's user avatar
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Training a RL agent using different data at each episode

I am training a RL agent whose state is composed of two numbers, ranging between 4 ~ 16 and 0 ~ 360. The action is continuous and between 0~90. In real life, the states can be any I am training a TD3 ...
Leibniz's user avatar
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What do we actually 'approximate' when dealing with large state spaces in Q-learning?

I realized that my state space is very large in size. I had planned to use tabular Q-learning (Bellman equation to update the $Q(s, a)$ after each action taken). But this 'large space' realization has ...
knowledge_seeker's user avatar