Questions tagged [value-function]

For questions related to the concept of value (or performance, or quality, or utility) function (as defined in reinforcement learning and other AI sub-fields). An example of this type of functions is the Q function (used e.g. in the Q-learning algorithm).

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What is the relationship between the Q and V functions?

Suppose we have a policy $\pi$ and we use SARSA to evaluate $Q^\pi(s, a)$, where $a$ is the policy $\pi$. Can we say that $Q^\pi(s, a) = V^\pi(s)$? The reason why I think this can be the case is ...
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Understanding V- and Q-functions

Assume the existence of a Markov Decision Process consisting of: State space $S$ Action space $A$ Transition model $T: S \times A \times S \to [0,1]$ Reward function $R: S \times A \times S \to \...
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32 views

What is the purpose of the arrow $\leftarrow$ in this formula?

What is the purpose of the arrow $\leftarrow$ in the formula below? $$V(S_t) \leftarrow V(S_t) + \alpha \left[ G_t - V(S_t) \right]$$ I presume it's not the same as 'equals'.
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103 views

Why is the state-action value function used more than the state value function?

In reinforcement learning, the state-action value function seems to be used more than the state value function. Why is it so?
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26 views

Value-based methods for 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 ...
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99 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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4answers
110 views

How to stop DQN Q function from increasing during learning?

Following the DQN algorithm with experience replay: We calculate the $loss=(Q(s,a)-(r+Q(s+1,a)))^2$. Assume I have positive but changing rewards. Meaning, $r>0$. Thus, since the rewards are ...
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123 views

How can a collaboration game be defined mathematically? [closed]

One of the common conceptions in AI is the idea of game theory. We see that in the predominance of chess and other games in the literature as metrics of AI success. We see it in the names of machine ...
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54 views

Is there any grid world dataset or generator for reinforcement learning?

I would like to start programming a multi task reinforcement learning model. For this, I need not just one maze or grid world (or just model-based), but many with different reward functions. So, I am ...
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58 views

Why is the state value function sufficient to determine the policy if a model is available?

In section "5.2 Monte Carlo Estimation of Action Values" of the second edition of the reinforcement learning book by Sutton and Barto, this is stated: If a model is not available, then it is ...
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214 views

What are the value functions used in reinforcement learning?

In reinforcement learning, we often define two functions, the state-value function $$V^\pi(s) = \mathbb{E}_{\pi} \left[\sum_{k=0}^{\infty} \gamma^{k}R_{t+k+1} \Bigg| S_t=s \right]$$ and the state-...
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172 views

An example of a unique value function which is associated with multiple optimal policies

In the 4th paragraph of http://www.incompleteideas.net/book/ebook/node37.html it is mentioned: Whereas the optimal value functions for states and state-action pairs are unique for a given MDP, ...