Questions tagged [value-iteration]

For questions related to the value iteration algorithm, which is a dynamic programming (DP) algorithm used to solve an MDP, that is, it is used to find a policy given the transition and reward functions of the MDP. Value iteration is related to another DP algorithm called policy iteration.

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1answer
25 views

Why do we need to go back to policy evaluation after policy improvement if the policy is not stable?

Above is the algorithm for Policy Iteration from Sutton's RL book. So, step 2 actually looks like value iteration, and then, at step 3 (policy improvement), if the policy isn't stable it goes back to ...
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1answer
95 views

Value Iteration failing to converge to optimal value function in Sutton-Barto's Gambler problem

In Example 4.3:Gambler's Problem of Sutton and Barto's book whose code is given here. In this code the value function array is initialized as np.zeros(states) where ...
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1answer
45 views

Why doesn't value iteration use $\pi(a \mid s)$ while policy evaluation does?

I was looking at the Bellman equation, and I noticed a difference between the equations used in policy evaluation and value iteration. In policy evaluation, there was the presence of $\pi(a \mid s)$, ...
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1answer
50 views

Is value iteration stopped after one update of each state?

In section 4.4 Value Iteration, the authors write One important special case is when policy evaluation is stopped after just one sweep (one update of each state). This algorithm is called value ...
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2answers
76 views

Why are policy iteration and value iteration studied as separate algorithms?

In Sutton and Barto's book about reinforcement learning, policy iteration and value iterations are presented as separate/different algorithms. This is very confusing because policy iteration includes ...
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2answers
147 views

What is the value of a state when there is a certain probability that agent will die after each step?

We assume infinite horizon and discount factor $\gamma = 1$. At each step, after the agent takes an action and gets its reward, there is a probability $\alpha = 0.2$, that agent will die. The assumed ...
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0answers
84 views

Problem in understanding equation given for convergence of TD(n) algorithm

Given equation 7.3 of Sutton and Barto's book for convergence of TD(n): $\max_s|\mathbb{E}_\pi[G_{t:t+n}|S_t = s] - v_\pi(s)| \leqslant \gamma^n \max_s|V_{t+n-1}(s) - v_\pi(s)|$ $\textbf{PROBLEM ...
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1answer
68 views

Is the PyTorch official tutorial really about Q-learning?

I read Q-learning algorithm and also I know value iteration (when you update action values). I think the PyTorch example is value iteration rather than Q-learning. Here is the link: https://pytorch....
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1answer
76 views

Would you categorize policy iteration as an actor-critic reinforcement learning approach?

One way of understanding the difference between value function approaches, policy approaches and actor-critic approaches in reinforcement learning is the following: A critic explicitly models a value ...
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1answer
42 views

What is generalized policy iteration?

I am reading Sutton and Barto's material now. I know value iteration, which is an iterative algorithm taking the maximum value of adjacent states, and policy iteration. But what is generalized policy ...
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1answer
76 views

Why do value iteration and policy iteration obtain similar policies even though they have different value functions?

I am trying to implement value and policy iteration algorithms. My value function from policy iteration looks vastly different from the values from value iteration, but the policy obtained from both ...
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1answer
63 views

Why do I need an initial arbitrary policy to implement value iteration algorithm

I've been recently given an assignment based on Reinforcement Learning and I'm supposed to implement the value iteration algorithm in a grid environment. The assignment: My doubt is why do I even ...
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1answer
18 views

Unable to understand V* at infinite time horizon using Bellman equation for solving MDP

I've been following the Berkeley cs188's assignment (I'm not taking the course). Currently, they don't show the solution in the gradescope unless I get it correct. My reasoning was $V^*(a)$ = 10 ...
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0answers
96 views

What is the difference between value iteration and policy iteration? [duplicate]

In reinforcement learning, what is the difference between policy iteration and value iteration? As much as I understand, in value iteration, you use the Bellman equation to solve for the optimal ...
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1answer
342 views

Q Learning for FrozenLake environment not converging to V* values from Value Iteration

I am trying to learn tabular Q learning, value iteration using the classical algorithms (no neural networks) by using a table of states and actions. I was trying it out on FrozenLake environment in ...
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0answers
92 views

Is Value Iteration better than Policy Iteration for first few iterations?

In Policy Iteration (PI), the action generated by the policy, whether it's optimal or not w.r.t the current value function $v(s)$. Whereas, in Value Iteration, the action is greedily generated w.r.t ...
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1answer
39 views

Can policy iteration use only the immediate reward for updates?

Is it still a policy iteration algorithm if the policy is updated optimizing a function of the immediate reward instead of the value function?
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1answer
55 views

Can I have different rewards for a single action based on which state it transitions to?

I am working on an MDP where there are four states and ten actions. I am supposed to derive the optimal policy to reach the desired state. At any state, a particular action can take you to any of the ...
3
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1answer
300 views

Understanding the update rule for the policy in the policy iteration algorithm

Consider the grid world problem in RL. Formally, policy in RL is defined as $\pi(a|s)$. If we are solving grid world by policy iteration then the following pseudocode is used: My question is related ...
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1answer
234 views

Unable to understand the second iteration update in value iteration algorithm for solving MDP

I am trying to understand the value iteration method for Markov Decision Process(MDP) and I was referring ot UC Berkeley's slides titled Markov Decision Processes and Exact Solution Methods On slide ...
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1answer
307 views

A few questions regarding the difference between policy iteration and value iteration

The question already has some answer. But I am still finding it quite unclear (also does $\pi(s)$ here mean $q(s,a)$ ?): The few things I do not understand are: Why the difference between 2 ...
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1answer
131 views

How do I apply the value iteration algorithm when there are two goal states?

I am working through the famous RL textbook by Sutton & Barto. Currently, I am on the value iteration chapter. To gain better understanding, I coded up a small example, inspired by this article. ...
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1answer
2k views

What is the time complexity of the value iteration algorithm?

Recently, I have come across the information (lecture 8 and 9 about MDPs of this UC Berkeley AI course) that the time complexity for each iteration of the value iteration algorithm is $\mathcal{O}(|S|^...
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1answer
2k views

Should the reward or the Q value be clipped for reinforcement learning

When extending reinforcement learning to the continuous states, continuous action case, we must use function approximators (linear or non-linear) to approximate the Q-value. It is well known that non-...
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1answer
84 views

In reinforcement learning, Is the optimal value (V*) corresponding to performing the best action in a given state?

it seems I am a little confused about the optimal value (V*) and optimal action-value (Q*) in reinforcement learning and just want some clarity because some blogs I read on Medium and GitHub are ...
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1answer
126 views

Reinforcement Learning (Fitted Q): Qn on Concept & Implementation

I hope to get some clarifications on Fitted Q-Learning ('FQL'). My Research So Far I've read Sutton's book (specifically, chp 6 to 10), Ernst et al and this paper. I know that ...
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1answer
186 views

Why can't we apply value iteration when we do not know the reward and transition functions, and how does Q-learning solve this issue?

I don't understand why we can't apply value iteration when don't know the reward and transition probabilities. In this lecture, the lecturer says it has to do with not being able to take max with ...