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1. As written in the book by Sutton and Barto, the Bellman equation is an approach towards solving the term of "optimal control".
2. If you have read anything related to reinforcement learning you must have encountered bellman equation somewhere.
3. Bellman equation is the basic block of solving reinforcement learning and is omnipresent in RL.
4. This is the bellman equation in the deterministic environment (discussed in part 1).
5. This example based on a naïve Environment pursues that the reader realises the complexity of this optimality problem and prepares him or her to see the importance of the Bellman equation.
6. Unfortunately, in most scenarios, we do not know the probability P and the reward r, so we cannot solve MDPs by directly applying the Bellman equation.
7. In the future posts of this series, we will show examples of how to use the Bellman equation for optimality.
8. To understand the Bellman equation, several underlying concepts must be understood.
9. The relationship between these two value functions is called the "Bellman equation".
10. The Bellman equation is classified as a functional equation, because solving it means finding the unknown function V, which is the value function.
11. Martin Beckmann also wrote extensively on consumption theory using the Bellman equation in 1959.
12. Because is the value function for a policy, it must satisfy the self-consistency condition given by the Bellman equation for state values (3.10).
13. Since the game has about states, it would take thousands of years on today's fastest computers to solve the Bellman equation for , and the same is true for finding .
14. In reinforcement learning, an algorithm that allows an agent to learn the optimal Q-function of a Markov decision process by applying the Bellman equation.
15. All four of the value functions obey special self-consistency equations called Bellman equations.
16. The basic idea behind the Bellman equations is this: The value of your starting point is the reward you expect to get from being there, plus the value of wherever you land next.
17. In this article, I am going to explain the Bellman equation, which is one of the fundamental elements of reinforcement learning.
18. Obviously, the goal of reinforcement learning is to maximize the long-term reward, so the Bellman equation can be used to calculate whether we have achieved the goal.

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• [{'LOWER': 'bellman'}, {'LEMMA': 'equation'}]