Learn Reinforcement Learning (2) - DQN · greentec's blog

In the previous article, we introduced concepts such as discount rate, value function, as well as time to learn reinforcement learning for the first time. The two concepts are summarized again as follows. Since the value function represents the value of a state as a number, we have looked at the value function for each state space on the Grid World and then the target can easily be found by a simple method of moving toward a grid with higher value. Figure 1. Calculate value functions for each state in Grid World The calculation of the value function was determined by repeated calculation until the value of each state did not change. Grid World, discussed in the last article, has fixed the location of objects and targets except the agent, so once you have calculated the value function, you could solve it easily by solving the problem. But there are two problems here. First, even in a fixed environment, if the size of the environment is extremely large, the size of the memory for storing

Learn Reinforcement Learning (2) - DQN · greentec's blog greentec's blog (@greentecq) © 2024 greentec.github.io All rights reserved. greentec's blog game designer, scripter, researcher en kr Learn Reinforcement Learning (2) - DQN 01 Apr 2019 • 0 Comments [ reinforcement-learning ] Value Function and Discount Rate ball-find-3 Q Function DQN Value Function and Discount Rate In the previous article , we introduced concepts such as discount rate, value function, as well as time to learn reinforcement learning for the first time. The two concepts are summarized again as follows. Discoun

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