What is discount value iteration?
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. For example consider γ = 0.9 and a reward R = 10 that is 3 steps ahead of our current state.
What is the role of a discount rate in decision making?
The discount rate is the interest rate used to determine the present value of future cash flows in a discounted cash flow (DCF) analysis. This helps determine if the future cash flows from a project or investment will be worth more than the capital outlay needed to fund the project or investment in the present.
What does higher discount rate mean?
In general, a higher the discount means that there is a greater the level of risk associated with an investment and its future cash flows. In other words, future cash flows are discounted back at a rate equal to the cost of obtaining the funds required to finance the cash flows.
How is the value iteration algorithm used in 1D?
In this article, we have explored Value Iteration Algorithm in depth with a 1D example. This algorithm finds the optimal value function and in turn, finds the optimal policy. We will go through the basics before going into the algorithm.
Why is the discount factor important in algorithms?
This helps proving the convergence of certain algorithms. In practice, the discount factor could be used to model the fact that the decision maker is uncertain about if in the next decision instant the world (e.g., environment / game / process ) is going to end.
When does a value iteration algorithm converge on an optimal policy?
While value-iteration algorithm keeps improving the value function at each iteration until the value-function converges. Since the agent only cares about the finding the optimal policy, sometimes the optimal policy will converge before the value function.
How does policy iteration and value iteration work?
Value-iteration and policy iteration rely on these equations to compute the optimal value-function. Value iteration computes the optimal state value function by iteratively improving the estimate of V (s). The algorithm initialize V (s) to arbitrary random values. It repeatedly updates the Q (s, a) and V (s) values until they converges.