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 ...
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
