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Add MC and TD to Unit2 glossary

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Katz, Ilia (ik216a)
2023-08-11 19:24:20 +03:00
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units/en/unit2/glossary.mdx
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@@ -32,6 +32,12 @@ This is a community-created glossary. Contributions are welcomed!
- **Off-policy algorithms:** A different policy is used at training time and inference time
- **On-policy algorithms:** The same policy is used during training and inference
### Monte Carlo and Temporal Difference learning strategies
- **Monte Carlo (MC):** Learning at the end of the episode. With Monte Carlo, we wait until the episode ends and then we update the value functin (or policy function) from a complete episode.
- **Temporal Difference (TD):** Learning at each step. With Temporal Difference Learning, we update the value function (or policy function) at each step without requiring a complete episode.
If you want to improve the course, you can [open a Pull Request.](https://github.com/huggingface/deep-rl-class/pulls)
This glossary was made possible thanks to: