Understanding Reinforcement Learning with Neural Networks Part 3: Guessing the Ideal Output

Short summary

Part 3 of a reinforcement learning series explains how to determine ideal outputs when correct answers aren't known upfront. Using a neural network example that chooses between locations based on hunger state, it demonstrates probability representation, probabilistic action sampling, and loss calculation via guessed correct actions. The approach enables gradient-based optimization.

• •Explains RL's core challenge: setting ideal outputs when ground truth is unknown
• •Demonstrates probability visualization and sampling techniques for action selection
• •Shows loss calculation based on guessed correct actions to enable learning

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