Questions tagged [pomdp]

For questions related to the concept of Partially Observable Markov Decision Process (POMDP), which is a generalization of the Markov Decision Process (MDP) to the cases where information about the states is incomplete (or partially observable).

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1answer
35 views

Is there a mathematical formalism to deal with a missing reward signal?

Typically, a Reinforcement Learning learning problem is formalized as finding an optimal policy for a Markov Decision Process (MDP). In many real-life situations, however, an agent can only get ...
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0answers
65 views

What algorithms minimize the log likelihood of sequential latent probabilistic models?

I am reading this paper, wherein they represent their model using a POMDP. Here is their latent probabilistic model that they have fit to the training data - Over here, $z_t \in Z$ represents the ...
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1answer
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Can a Reinforcement Learning problem with multiple simultaneous actions be formalized as a Multiagent Partially Observable Markov Decision Process?

Consider the following decision making problem. We have a controller that selects locations from a grid of coordinates and captures an image (observation $o_t$) with a camera at each location (action $...
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3answers
297 views

What exactly are partially observable environments?

I have trouble understanding the meaning of partially observable environments. Here's my doubt. According to what I understand, the state of the environment is what precisely determines the next state ...
2
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1answer
29 views

Is there a way of path reconstruction using only the history of belief state?

Given a history of belief states, is there a common method that backtracks the most likely path of ending up in the current belief state? I have a Markov model which calculates belief states after ...
2
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0answers
28 views

Are there any known disadvantages of implementing vanilla Q-learning on a discretized-state space environment?

For an RL problem on a continuous state space, the states could be discretized into buckets and these buckets used in implementing the Q-table. I see that is what is done here. However, according to ...
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0answers
72 views

How do I learn the value function for a POMDP with a single-step horizon (bandit)?

Consider a POMDP with a finite number of environment states, $|\mathcal{S}| = N$, but the number of belief states is uncountably infinite. The belief state space is the convex hull of an $N$ simplex. ...
4
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0answers
75 views

How to update the observation probabilities in a POMDP?

How can I update the observation probability for a POMDP (or HMM), in order to have a more accurate prediction model? The POMDP relies on observation probabilities that match an observation to a state....
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0answers
15 views

How to obtain the policy in the form of a finite-state controller from the value function vectors over the belief space of the POMDP?

I was reading this paper by Hansen. It says the following: A correspondence between vectors and one-step policy choices plays an important role in this interpretation of a policy. Each vector in $\...
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0answers
24 views

Understanding example for Improved Policy Iteration for POMDPs

I was going through this paper by Hansen. This paper proposes policy improvement by first converting set of $\alpha$ vectors into finite state controller and then comparing them to obtain improved ...
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0answers
57 views

How does one stack multiple observations in the input layer of a convolutional neural network?

The paper, Deep Recurrent Q-Learning for Partially Observable MDPs, talks about stacking multiple observations in the input of a convolutional neural network. How does this exactly work? Do the ...
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1answer
183 views

What is the difference between Bayes-adaptive MDP and a Belief-MDP in Reinforcement Learning?

I have been reading a few papers in this area recently and I keep coming across these two terms. As far as I'm aware, Belief-MDPs are when you cast a POMDP as a regular MDP with a continuous state ...
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0answers
59 views

If the performance of an RL agent in a partially observable environment is "good", is this likely only accidental?

In my research, I remember to have read that, in case of an environment which can be modeled by partially observable MDP, there are no convergence guarantees (unfortunately, I do not find the paper ...
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2answers
95 views

Does "transition model" alone in an MDP imply it's non-deterministic?

I am looking at a lecture on POMDP, and the context is that, when the quadcopter can't see the landmarks, it has to use reckoning. And then he mentions the transition model is not deterministic, hence ...
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0answers
43 views

How to choose an RL algorithm for a Gridworld that models a much more complex problem

I am considering using Reinforcement Learning to do optimal control of a complex process that is controlled by two parameters $(n_O, n_I), \quad n_I = 1,2,3,\dots, M_I, n_O = 1,2,3,\dots, M_O$ In this ...
2
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0answers
68 views

Can we use a Gaussian process to approximate the belief distribution at every instant in a POMDP?

Suppose $x_{t+1} \sim \mathbb{P}(\cdot | x_t, a_t)$ denotes the state transition dynamics in a reinforcement learning (RL) problem. Let $y_{t+1} = \mathbb{P}(\cdot | x_{t+1})$ denote the noisy ...
3
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0answers
82 views

Is Monte Carlo tree search needed in partially observable environments during gameplay?

I understand that with a fully observable environment (chess / go etc) you can run an MCTS with an optimal policy network for future planning purposes. This will allow you to pick actions for gameplay,...
3
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1answer
96 views

What is the intuition behind grid-based solutions to POMDPs?

After spending some time reading about POMDP, I'm still having a hard time understanding how grid-based solutions work. I understand the finite horizon brute-force solution, where you have your ...
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0answers
73 views

Why can the core reinforcement learning algorithms be applied to POMDPs?

Why can an AI, like AlphaStar, work in StarCraft, although the environment is only partially observable? As far as I know, there are no theoretical results on RL in the POMDP environment, but it ...
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0answers
58 views

How exactly does self-play work, and how does it relate to MCTS?

I am working towards using RL to create an AI for a two-player, hidden-information, a turn-based board game. I have just finished David Silver's RL course and Denny Britz's coding exercises, and so am ...
2
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0answers
39 views

What are some approaches to estimate the transition and observation probabilities in POMDP?

What are some common approaches to estimate the transition or observation probabilities, when the probabilities are not exactly known? When realizing a POMDP model, the state model needs additional ...
2
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0answers
25 views

Finding total number of states in a POMDP

I've been working on a question that is posed in a document I've been reading, that models qualifying for a job as a POMDP. In this model, a person takes 3 exams, and must pass all of them in order to ...
6
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2answers
224 views

Are perfect and imperfect information games modelled as fully and partially observable environments, respectively?

In perfect information games, the agent can see all the moves performed in the past. Besides, it can observe the next action that will be put into practice by the opponent. In this case, can we say ...
2
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1answer
149 views

Is it possible for value-based methods to learn stochastic policies?

Is it possible for value-based methods to learn stochastic policies? I'm trying to get a clear picture of the different categories for RL algorithms, and while doing so I started to think about ...
4
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0answers
167 views

Is there a way to do reinforcement learning in POMDP?

Are there any algorithms to use reinforcement learning to learn optimal policies in partially observable Markov decision process (POMDP) i.e. when the state is not perfectly observed. More ...
7
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1answer
3k views

Can Q-learning be used in a POMDP?

Can Q-learning (and SARSA) be directly used in a Partially Observable Markov Decision Process (POMDP)? If not, why not? My intuition is that the policies learned will be terrible because of partial ...
2
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1answer
69 views

Can the normalization factor for the belief state update be zero?

In order to update the belief state in a POMDP, the following formula is used: $$b'(s')=\frac{O(a, s', z) \sum_{s\in S} b(s)T(s, a, s')}{\mathbb{P}(z \mid b, a)}$$ where $s$ is a specific state in ...
2
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1answer
79 views

Does the observation function for POMDP always add up to 1?

I was reading in the article A tutorial on partially observable Markov decision processes (p. 120), by Michael L. Littman, that $\sum_{z \in Z}O(a, s',z) =1$, where $a$ is the action, $s'$ the next ...
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1answer
1k views

Is my understanding of the differences between MDP, Semi MDP and POMDP correct?

I just wanted to confirm that my understanding of the different Markov Decision Processes are correct, because they are the fundamentals of reinforcement learning. Also, I read a few literature ...
2
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2answers
144 views

How to define a reward function in POMDPs?

How do I define a reward function for my POMDP model? In the literature, it is common to use one simple number as a reward, but I am not sure if this is really how you define a function. Because this ...