Questions tagged [markov-decision-process]
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3
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1answer
45 views
Should I model my problem as a semi-MDP?
I have a system (like a bank) that people (customers) are entered into the systems by a Poisson process, so the time between the arrival of people (two consecutive customers) will be a random variable....
0
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0answers
9 views
How can we estimate the transition model and reward function?
In reinforcement learning (RL), there are model-based and model-free algorithms. In short, model-based algorithms use a transition model (e.g. a probability distribution) and the reward function, even ...
3
votes
3answers
142 views
Can the rewards be stochastic when the transition model is deterministic?
Suppose we have a deterministic environment where knowing $s,a$ determines $s'$. Is it possible to get two different rewards $r\neq r'$ in some state $s_{\text{fixed}}$? Assume that $s_{\text{fixed}}$ ...
1
vote
1answer
34 views
What is the relation between a policy which is the solution to a MDP and a policy like $\epsilon$-greedy?
In the context of reinforcement learning, a policy, $\pi$, is often defined as a function from the space of states, $\mathcal{S}$, to the space of actions, $\mathcal{A}$, that is, $\pi : \mathcal{S} \...
1
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2answers
41 views
How are the reward functions $R(s)$, $R(s, a)$ and $R(s, a, s')$ equivalent?
In this video, the lecturer states that $R(s)$, $R(s, a)$ and $R(s, a, s')$ are equivalent representations of the reward function. Intuitively, this is the case, according to the same lecturer, ...
1
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0answers
31 views
What limitations does the Markov property place on real time learning?
The Markov property is the dependence of a system's future state probability distribution solely on the present state, excluding any dependence on past system history.
The presence of the Markov ...
1
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2answers
124 views
Is Monte Carlo Tree Search appropriate for problems with large state and action spaces?
I'm doing a research on a finite-horizon Markov decision process with $t=1, \dots, 40$ periods. In every time step $t$, the (only) agent has to chose an action $a(t) \in A(t)$, while the agent is in ...
5
votes
1answer
60 views
What is the appropriate approach to playing a game with incomplete state information?
I have a steady hex-map and turn-based war game featuring WWII carrier battles. I would like to improve the fixed policy for the AI using reinforcement learning. I have some beginner's questions, ...
0
votes
1answer
68 views
Importance Sampling Ratio Probability
When reading Reinforcement Learning by Sutton and Barto, I came across the importance sampling ratio.
The first equation, I believe, describes the probability a particular sequence is obtained given ...
1
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0answers
19 views
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 $\mathcal{A}$, $...
