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1. www.incompleteideas.net › book › ebook3.7 Value Functions - incompleteideas.net

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   3.8 Optimal Value Functions Up: 3. The Reinforcement Learning Previous: 3.6 Markov Decision Processes Contents 3.7 Value Functions. Almost all reinforcement learning algorithms are based on estimating value functions--functions of states (or of state-action pairs) that estimate how good it is for the agent to be in a given state (or how good it is to perform a given action in a given state).

2. stackoverflow.com › questions › 44157418Understanding policy and value functions reinforcement learning

   stackoverflow.com › questions › 44157418

   May 25, 2017 · A value function determines the best course of actions to achieve highest reward. So I have a random policy. I get the value function. I update my policy with a new distribution according to the value function. I get a value function of this new updated policy and reevaluate once again.

3. blog.mlq.ai › reinforcement-learning-policiesFundamentals of Reinforcement Learning: Policies, Value ...

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   In this section, we'll look at how to derive the Bellman equation for state-value functions, action-value functions, and understand how it relates current and future values. State-Value Bellman Equation. The Bellman equation for the state-value equation defines the relationship between the value of a state and the value of future possible states.

4. towardsdatascience.com › reinforcement-learningReinforcement Learning: Bellman Equation and Optimality (Part 2)

   towardsdatascience.com › reinforcement-learning

   Aug 30, 2019 · State-Action Value Function from the Backup Diagram. So, this is how we can formulate Bellman Expectation Equation for a given MDP to find it’s State-Value Function and State-Action Value Function. But, it does not tell us the best way to behave in an MDP. For that let’s talk about what is meant by Optimal Value and Optimal Policy Function.

5. spinningup.openai.com › en › latestPart 1: Key Concepts in RL — Spinning Up documentation - OpenAI

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   The crucial difference between the Bellman equations for the on-policy value functions and the optimal value functions, is the absence or presence of the over actions. Its inclusion reflects the fact that whenever the agent gets to choose its action, in order to act optimally, it has to pick whichever action leads to the highest value.

6. medium.com › iecse-hashtag › rl-part-2-returnsPart 2 — Returns, Policy and Value Functions - Medium

   medium.com › iecse-hashtag › rl-part-2-returns

   May 23, 2020 · action value function Similarly, the action-value function for policy π, denoted as qπ, tells us how good it is for the agent to take any given action from a given state while following policy π.

7. People also ask

   Does a value function determine the best course of actions?

   • You have a policy, which is effectively a probability distribution of actions for all my states. A value function determines the best course of actions to achieve highest reward. No. A value function tells you, for a given policy, what the expected cumulative reward of taking action a in state s is.

   Understanding policy and value functions reinforcement learning

   stackoverflow.com/questions/44157418/understanding-policy-and-value-functions-reinforcement-learning

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   What are value functions?

   • Of course the rewards the agent can expect to receive in the future depend on what actions it will take. Accordingly, value functions are defined with respect to particular policies. Recall that a policy, , is a mapping from each state, , and action, , to the probability of taking action when in state .

   3.7 Value Functions - incompleteideas.net

   www.incompleteideas.net/book/ebook/node34.html

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   What is a value function in a policy?

   • Accordingly, value functions are defined with respect to particular policies. Recall that a policy, , is a mapping from each state, , and action, , to the probability of taking action when in state . Informally, the value of a state under a policy , denoted , is the expected return when starting in and following thereafter.

   3.7 Value Functions - incompleteideas.net

   www.incompleteideas.net/book/ebook/node34.html

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   What is an action-value function of a state?

   • An action-value function of a state describes the expected return if the agent selects action A A with respect to policy π π: qπ(s,a) ≐ Eπ[Gt|St = s,At = a] q π (s, a) ≐ E π [G t | S t = s, A t = a] In summary, value functions vπ(s) v π (s) allow an agent to predict future rewards, instead of long-term outcome.

   Fundamentals of Reinforcement Learning: Policies, Value Functions

   blog.mlq.ai/reinforcement-learning-policies-value-functions-bellman-equation/

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   How good is a value function?

   • The notion of "how good" here is defined in terms of future rewards that can be expected, or, to be precise, in terms of expected return. Of course the rewards the agent can expect to receive in the future depend on what actions it will take. Accordingly, value functions are defined with respect to particular policies.

   3.7 Value Functions - incompleteideas.net

   www.incompleteideas.net/book/ebook/node34.html

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   What is state-action value function (Q-function)?

   • The above equation tells us that the value of a particular state is determined by the immediate reward plus the value of successor states when we are following a certain policy (π). Similarly, we can express our state-action Value function (Q-Function) as follows :

   Reinforcement Learning: Bellman Equation and Optimality (Part 2)

   towardsdatascience.com/reinforcement-learning-markov-decision-process-part-2-96837c936ec3

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8. towardsdatascience.com › reinforcement-learningReinforcement Learning — The Value Function | by Jingles ...

   towardsdatascience.com › reinforcement-learning

   Jun 30, 2019 · The value function is the algorithm to determine the value of being in a state, the probability of receiving a future reward. The value of each state is updated reversed chronologically through the state history of a game, with enough training using both explore and exploit strategy , the agent will be able to determine the true value of each state in the game.