# 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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### How to derive "value iteration" from "policy iteration"?

This is the equation for "value iteration" from Sutton-Barto: \begin{align} v_{k+1}(s)=& \max_{a \in \mathcal{A}}\mathbb{E} \Big(R_{t+1}+\gamma v_k(S_{t+1}) \big|S_t=s, A_t=a\Big) \\ =&...
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### What are the similarities between Q-learning and Value Iteration?

This is the explanation of value iteration in our notes where you keep applying bellman optimality equation till it stops changing and then acting greedily wrt the value function gives the optimal ...
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### Why solely a one-step-lookahead in value/policy-iteration?

In value iteration and policy iteration we solely consider a one-step-lookahead where the lookahead is from the previous iteraiton and therefore need to sweep over all states and iterate this ...
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1 vote
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### Can we use Low rank approximation in Markov decision process problem?

I am newbie in MDP.I have started reading Ronald Howard Dynamic Programming and MDP book as well as Sutton and Barto An Introduction to Reinforcement Learning. To my understanding MDP is a model based ...
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### Are the relative magnitudes of the learned and optimal state value function the same?

I have been reading recently about value and policy iteration. I tried to code the algorithms to understand them better and in the process I discovered something and I am not sure why is the case (or ...
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### 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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1 vote
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### 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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### 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 $\pi(a \mid s)$, ...
371 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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### 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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### 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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### 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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### How is the fitted Q-iteration algorithm related to $Q^*(s, a)$, and how can we use function approximation with this algorithm?

I hope to get some clarifications on Fitted Q-Iteration (FQI). My Research So Far I've read Sutton's book (specifically, ch 6 to 10), Ernst et al and this paper. I know that $Q^*(s, a)$ expresses the ...
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