Does the optimal policy depend on the discount factor?

Does the optimal policy depend on the discount factor? An initial policy with action a in both states leads to an unsolvable problem. … However, the choice of discount factor will affect the policy that results.

What is the optimal policy?

In a finite Markov Decision Process (MDP), the optimal policy is defined as a policy that maximizes the value of all states at the same time¹. In other words, if an optimal policy exists, then the policy that maximizes the value of state s is the same as the policy that maximizes the value of state s’.

What role does the discount factor play in reinforcement learning algorithms?

The discount factor essentially determines how much the reinforcement learning agents cares about rewards in the distant future relative to those in the immediate future. If γ=0, the agent will be completely myopic and only learn about actions that produce an immediate reward.

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What does the discount factor do in RL?

What is the role of the discount factor in RL? The discount factor, , is a real value ∈ [0, 1], cares for the rewards agent achieved in the past, present, and future. In different words, it relates the rewards to the time domain. … If = 1, the agent cares for all future rewards.

What is a discount factor in Markov decision process?

Discount factor is a value between 0 and 1. A reward R that occurs N steps in the future from the current state, is multiplied by γ^N to describe its importance to the current state.

Is optimal policy unique in MDP?

In chapter 3.8 of the book “Reinforcement Learning: An Introduction” (by Andrew Barto and Richard S. Sutton) it is stated that there always exists at least one optimal policy, but it doesn’t prove why.

Does optimal policy always exist for MDP?

The results below assume finite state, action space and bounded rewards. Theorem 1 (Puterman [1994], Theorem 6.2. 7). For any infinite horizon discounted MDP, there always exists a deterministic stationary policy π that is optimal.

What is the discount factor equal to?

The basic formula for determining this discount factor would then be D=1/(1+P)^N, which would read that the discount factor is equal to one divided by the value of one plus the periodic interest rate to the power of the number of payments.

What is difference between reward & discount factor?

A discount factor will result in state/action values representing the immediate reward, while a higher discount factor will result in the values representing the cumulative discounted future reward an agent expects to receive (behaving under a given policy).

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Is Q-learning reinforcement learning?

Q-learning is a model-free reinforcement learning algorithm. Q-learning is a values-based learning algorithm. Value based algorithms updates the value function based on an equation(particularly Bellman equation). … Means it learns the value of the optimal policy independently of the agent’s actions.

What is the reinforce algorithm?

REINFORCE belongs to a special class of Reinforcement Learning algorithms called Policy Gradient algorithms. The objective of the policy is to maximize the “Expected reward”. … Each policy generates the probability of taking an action in each station of the environment.

What is Q learning in reinforcement learning?

Q-Learning is a basic form of Reinforcement Learning which uses Q-values (also called action values) to iteratively improve the behavior of the learning agent. Q-Values or Action-Values: Q-values are defined for states and actions. is an estimation of how good is it to take the action at the state .

What is the difference between value iteration and policy iteration?

In Value Iteration – You randomly select a value function , then find a new (improved) value function in an iterative process, until reaching the optimal value function , then derive optimal policy from that optimal value function . Policy iteration works on principle of “Policy evaluation —-> Policy improvement”.

What are the essential elements of MDP?

Four essential elements are needed to represent the Markov Decision Process: 1) states, 2) model, 3) actions and 4) rewards.

Which learning is both model based and having fixed policy?

Reinforcement Learning (RL) refers to learning to behave optimally in a stochastic environment by taking actions and receiving rewards (Sutton & Barto, 1998). The environment is assumed Markovian in that there is a fixed probability of the next state given the current state and the agent’s action.

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What is MDP model?

In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

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