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What is the difference between state value function and action value function?

What is the difference between state value function and action value function?

That means summarised, the state-value-function returns the value of achieving a certain state and the action-value-function returns the value for choosing an action in a state, whereas a value means the total amount of rewards until reaching terminal state.

What is state value in reinforcement learning?

More specifically, the state value function describes the expected return G_t from a given state. Furthermore an action-value function can be defined. The action-value of a state is the expected return if the agent chooses action a according to a policy π. Value functions are critical to Reinforcement Learning.

Which agent learns a policy that maps directly from state to action?

In policy optimization methods the agent learns directly the policy function that maps state to action. The policy is determined without using a value function.

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What is V in reinforcement learning?

Optimal State Value / Action Functions Think of it as the commander assessing the outcome (value V) of each situation (state s) . So we define the optimal value function V*(s) as the maximum values that can be obtained after checking all policies to V(s) for all states.

Where do we use Q function in reinforcement learning algorithm?

Q-Learning is a value-based reinforcement learning algorithm which is used to find the optimal action-selection policy using a Q function. Our goal is to maximize the value function Q. The Q table helps us to find the best action for each state.

Which of the following is a function of both state and action?

The reward function is generally a function of both state and action R(s, a). The transition function P(s |s, a) defines the probability of moving to s given s and a.

What is a policy reinforcement learning?

The Definition of a Policy Reinforcement learning is a branch of machine learning dedicated to training agents to operate in an environment, in order to maximize their utility in the pursuit of some goals.

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What is AQ function in RL?

Q Value (Q Function): Usually denoted as Q(s,a) (sometimes with a π subscript, and sometimes as Q(s,a; θ) in Deep RL), Q Value is a measure of the overall expected reward assuming the Agent is in state s and performs action a, and then continues playing until the end of the episode following some policy π.

What is the state-action value function?

Similarly, the state-action value function, Q π ( s, a), is the expected return of when starting in state s, taking action a, and following policy π thereafter. Read these 3 times out loud and you’ll get the difference.

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

In post 2 we extended the definition of state-value function to state-action pairs, defining a value for each state-action pair, which is called the action-value function, also known as Q-function or simply Q.

Does the value function V(S_t) depend on the policy?

No, the value function V(s_t)does not depend on the policy. You see in the equation that it is defined in terms of an action a_tthat maximizes a quantity, so it is not defined in terms of actions as selected by any policy.

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What is the state value function of [Math]V_\\Pi(s]?

The state value function, [math]V_\\pi(s)[/math], is the expected return when starting in state [math]s[/math] and following [math]\\pi[/math] thereafter.