Questions tagged [markov-decision-process]
For questions related to the concept of Markov decision process (MDP), which is a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision-maker. The concept of MDP is useful for studying optimization problems solved via dynamic programming and reinforcement learning.
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How can I find an upper bound on the number of iterations required to have less than $\varepsilon$ difference in the value of state?
I learned about the Value Iteration algorithm which can help find an optimal policy and values of an MDP with state rewards:
$$V_0(s)=R(s)$$
$$V_{t}(s)=R(s)+\gamma\cdot\underset{a}{max}\underset{s'}{\...
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Under which conditions does value iteration and policy iteration will give us the optimal solution if gamma equals 1
I’m learning about policy iteration and value iteration, and I’m wondering under which conditions does both algorithms will give us the optimal solution, if our discount factor (gamma) equals 1.
Note: ...
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Convergence of Value Iteration for Discount factor of 1
Given this pseudo code for value iteration:
In the case of gamma=1, under what conditions on the MDP will we still be able to find the optimal policy?
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Should DQN/PPO be used for state spaces that are not that large?
I'm interested in trying out Q-learning to solve a problem where I already have a simulation of the environment that can run at about 100,000 fps or steps/sec. Its also continuous with no terminal ...
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Does fixing an action in the state transition function of an MDP yield a transition matrix?
I stumbled across this seemingly elementary question while studying the theory of Markov Decision Processes. Suppose $\mathcal{M} = (\mathcal{S}, \mathcal{A}, \mathcal{P}, \mathcal{R})$ is an MDP and ...
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Should I model this problem as a POMDP?
Suppose we have a finite-horizon sequential decision-making problem. At period $t$ we are in state $s$. We take action $a$ and we receive reward $r$ and go to state $s-1$ at period $t+1$. However, it ...
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Would the optimal policy remain same, if I replace R with V*?
In the context of RL, say I'm performing Value Iteration on a reward function R1. And the converged optimal policy is P1 and values are V1.
Then, let's say I set rewards to be R2=V1 and perform value ...
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What is the difference between these two versions of the Bellman equation?
The first version is the one I am most familiar with:
$$V_\pi(s) = \sum_{a}^{}\pi(a|s) \sum_{s'}^{}T(s, a, s')[R(s, a, s') + \gamma V_\pi(s')]$$
where $T(s, a, s')$ represents the probability of ...
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What reward should be selected for transition states to make the agent reach the terminal state (destination) faster? negative, positive, or zero?
Consider the simple environment below, where the gray cells are the terminal states and the agent receives a reward of $-5$ for taking any action in these states. The nonterminal states are $S = \{1, ...
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MDP with a non-markovian reward function?
I have set up a RL environment and it converges to a decent solution when using a reward function:
$R(s_t,a_t) = fenv(s_t, a_t)$ , where $fenv$ is the environment dynamics.
Now, i want to change the ...
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How to formulate Monte Carlo Tree Search in a stochastic environment with a changing action space
Can we efficiently solve a problem in which:
the valid actions at any given time are changing
the environment is stochastic
we have an infinite time horizon
using MCTS?
To be more specific, I'm ...
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How can we approximate infinite horizon MDP with finite horizon MDP in the context of reinforcement learning?
For a given value of "discount factor" (and reward values' range) in fixed finite horizon markov decision process (MDP), upto how many episodes we have to extend this MDP so that we can ...
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How to correctly evaluate the state value of this simple markov decision process?
For some contexts, I'm working on a c# library for reinforcement learning. I implemented two methods to evaluate a state value function, namely the TD(0) method and the Monte Carlo first visit method.
...
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How to compare different trajecories in a Markov Decision Process
I realize that my question is a bit fuzzy and I am sorry for that. If needed, I will try to make it more rigorous and precice.
Let $\mathcal{M}$ be a Markov Decision Process, with state space $\...
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What is the relationship between state value and action Q function?
I am currently working of reformulation of Dec-POMDPs (where I recast a Dec-POMDP to a continuous state MDP for example). I am trying to prove that the action value (q-function) of the new MDP is ...
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Control variables and cofounding effects in stochastic programming/,model predictive control/reinforcement learning
How can we be sure that confounding variables/control variables don’t pickup the effect our decisions w.r.t decision variables had on the actual control variable?
Since the term control variable ...
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In the Policy Gradient Theorem proof, why is $d^\pi(s) = \sum_{k=0}^{\infty}\gamma^{k}Pr(s_0 \rightarrow s, k, \pi)$ true?
I was reading the original Policy Gradient Paper. I didn't quiet get the last step of the proof for the policy gradient theorem. The proof given in the paper is below:
I don't understand how the last ...
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What's the relationship between Bayesian RL and POMDPs?
Bayesian RL seems concerned with having uncertainty over the transition function of the environment, while POMDPs try to capture uncertainty over the state one is currently in. However, both end up ...
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How to partition the belief space of a POMDP using a "granularity" parameters?
as I understand, to a solve a pomdp we transform it into a belief-MDP. The value function for this belief-MDP is proven to be piecewise linear and convex (PWLC) [Smallwood and Sondik, 1973].To apply ...
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Markov's Decision Process - calculate value in each iteration
I have the following decision tree:
I calculated the value of the plan using the following paramenters (given):
{𝑆0 → 𝑎1 , 𝑆1 → 𝑎3 , 𝑆2 → 𝑎4 },
Discount factor (𝛾)= 0.2
I used this formula to ...
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How to prove that an action-value function optimal for one problem formulation is also optimal for another?
I want to ask about the intuition/where-to-look/what-to-try if I want to prove that an action value function optimal for a problem is also optimal for another reformulation of that smae problem. For ...
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Markov decision process how to get the correct policy if targets are reached once among N episodes?
I have implemented an MDP on a network such that an agent starts in a node, takes an action from a set of predefined actions and next node (including current). Some of the nodes would result in ...
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Remove already reached targets from the system to enable reaching other targets?
This may be a very fundamental question, but somehow I can't decide.
I have a graph and the user can take several actions while traversing it and there are multiple points with rewards. When I execute ...
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How to represent the state and action space when modeling card game Love Letter using Markov Decision Process
Now my task is to use model checking to find a best strategy for card game Love Letter, and I need to model the game using Markov Decison Process first.
I have done a lot of resaerch and I decided to ...
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What is the correct interpretation of the discount factor in MDPs?
In infinite-horizon MDPs one can consider the expected discounted return from a distribution of start states as the objective[^1]. i.e. $\mathbb{E}[V^{\pi}(S_0)] = \mathbb{E}[G_0] = \mathbb{E}[\sum_{t=...
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Rewrite the four Bellman equations for the four value functions $(v_{\pi},v_*,q_{\pi},q_*)$ in terms of $p$ (3.4) and $r$ (3.5) [duplicate]
I have done exercise 3.29 from Sutton and Barto and I'd like to check if it's correct. Here's the exercise:
Rewrite the four Bellman equations for the four value functions $(v_{\pi},v_*,q_{\pi},q_*)$ ...
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Exercise 3.21 Sutton Barto: Draw or describe the contours of the optimal action-value function for putting, $q_{*}(s, putter)$, for the golf example
I am doing exercise 3.21 in Sutton and Barto. Here's the exercise:
Draw or describe the contours of the optimal action-value function for
putting, $q_{*}(s, putter)$, for the golf example.
Here's ...
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Does maximizing the value function and maximizing the state-action value function generate the same optimal policy?
In reinforcement learning, we define the optimal policy $\pi^*$ as the policy that maximizes the value of the state:
$$
\pi_v^*=\underset{\pi}{\operatorname{argmax}} {V_{\pi}(s)}
$$
In Q-learning, we ...
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$E_{\pi}[R_{t+1}|S_t=s,A_t=a] = E[R_{t+1}|S_t=s,A_t=a]$?
I would like to solve the first question of Exercise 3.19 from Sutton and Barto:
Exercise 3.19 The value of an action, $q_{\pi}(s, a)$, depends on the expected next reward and
the expected sum of the ...
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Determining a policy to play a game of chance
I'm trying to optimize the expected return from a game of chance, but have quickly realized the problem outclasses the introductory AI course I took in college years ago. I would appreciate any ...
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Number of possible joint policies in a Dec-POMDP and the time required to evaluate each one
I was reading a book about Dec-POMDPs and came across this curious result where the author specifies the number of possible joint policies to evaluate and the time needed to evaluate a single joint ...
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Can the state transition function be dynamic in reinforcement learning?
In general, there are two types of transition functions in reinforcement learning. Mathematically, they are as follows
#1: Stochastic state transition function:
$$T : S \times A \times S \rightarrow [...
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Is it appropriate to represent 'total failure' as an absorbing state?
My understanding is that, in Markov decision processes, absorbing state are states which can transition only to themselves and that these transitions generate rewards of 0. I know that absorbing ...
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Does the policy search work if there is no state to state dependency through actions?
There is a game in which the state comes one after the other without depending on the agent's action. The agent gets a reward for its actions at the end of the game. The goal of the agent is to reach ...
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Does $S_{t+1}$ denote the future information in Q-learning?
In Q-learning, $Q(S_t,a)$ is updated by the Bellman equation. $Q(S_t,a) = r + \max_{a'}(Q(S_{t+1},a'))$ where $S_{t+1}$ is the future state.
Let's say $S$ denotes the stock price, does it mean we are ...
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Does $R_{s}=E[R_{t}|S_{t}=s]$ indicate the reward we might expect on getting on average moving from any other state to $s$?
I'm trying to understand correctly what each "variable" in RL is and I'm not sure about $R_{s}$ the reward function. I used to think that it's the reward we may expect on average after ...
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How are previous values updated when performing value iteration?
I have been trying to understand how you determine the value for each square in a grid world and I have seen/watched a few different examples to try and apply it to my own grid and I find myself ...
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Factors that affect the number of iterations of value iteration
I had an assumption that value iteration will take more iterations to converge if the map size increases/environment's complexity increases.
I tried to verify this idea by running value iteration on ...
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Are these two forms of the state value function the same?
Why are there different forms of the value function in reinforcement learning?
Sutton & Barto (2nd edition, equation 3.14) define the state value function as follows
$$v_{\pi}(s) = \displaystyle\...
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Does the state space of an MDP change in these two examples?
In the classic Atari environments, like that introduced in the original DQN paper, the state space is the set of all possible images that the Atari emulator can produce (or more generally just any RGB ...
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Are multi agent or self-play environments always automatically POMDPs?
As part of my thesis, I'm working on a zero sum game with RL to train an agent.
The game is a real-time game, a derivation of pong, one could imagine playing pong with both sides being foosball rods.
...
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Is there a fundamental difference between an environment being stochastic and being partially observable?
In AI literature, deterministic vs stochastic and being fully-observable vs partially observable are usually considered two distinct properties of the environment.
I'm confused about this because what ...
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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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Can RL still learn in a scenario where current state and the next state are independant?
I am trying to implement reinforcement learning into my real-world problem.
One thing making me hesitant to apply RL is that this real-world problem of mine is unique in a way how every state is ...
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Implementation of MDP in python to determine when to take action clean
I am trying to model the following problem as a Markov decision process.
In a steel melting shop of a steel plant, iron pipes are used. These pipes generate rust over time. Adding an anti-rusting ...
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Are there any deep RL algorithms that work well on finite MDPs and non-trivial terminal rewards?
I notice that most Deep Reinforcement Learning (DRL) works focus on Markov Decision Process (MDP) with an infinite time horizon.
Are there any algorithms that work well on finite MDP and non-trivial ...
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In reinforcement learning, why are policies defined as functions of states and not observations?
I am new to RL and I am following Sutton & Barto's book.
My doubt is, when we talk about the policy of our agent, we say it is the probability of taking some action $a$ given the state $s$. ...
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Discard irrelavant states from a MDP
I came across this question about MDP.
From the look of it, it seems the full MDP is reducible if the discarded state only have 1 way in and out but is it really so if we change the discounted factor? ...
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Calculating state-value functions in Markov Decision Process
I am watching David Silver's lectures on RL available on YouTube. My question here is with regard to Lecture 2 (Link to Video). At 1:11:00, I could not understand how he is calculating the state-value ...
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Is it really hard to learn in a stochastic environment?
I understand that a stochastic environment is one that does not always lead you to the desired state by giving a particular action $a$ (But the probability to change to a not desire state is fixed, ...
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