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Reinforcement Learning Training Pipeline for Game AI

Type FlowchartStandard Sugiyama layered DAG + orthogonal routingEngine schematex-flowchartUpdated 7/21/2026
Reinforcement Learning Training Pipeline for Game AI
Drawing preview
The scenario

Illustrates the iterative process of a deep Q-network (DQN) agent training in a game environment, from initialization to model saving.

What is in this drawing

Read the decisions behind it.

01

Is the replay buffer large enough to sample?

02

Should the target network be updated?

03

Has the episode terminated?

04

Have max episodes been reached?

When designing or explaining reinforcement learning training pipelines, particularly for game AI or simulation-based agents using experience replay.

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