 |
Next is Agent.
Because the agent uses DQN, it initializes the related MLP and DQN.
It also initializes observation space and action space to interact with the environment.
Learning agent is an agent for learning.
It receives state, return, and done information from the environment and selects an action.
At this time, the search method for selecting action is the epsilon greedy method.
This is to solve the problem of exploring only the initially learned q-value value.
The learned q-value is stored and managed.
Test agent is an agent to check the learned results.
The difference from the Learning agent is the use of the greedy method to check the best results.
Also, instead of learning and saving q-value, it brings up the stored value and stores the final result.
|
IEEE/ICACT20220153 Slide.12
[Big Slide]