3d42de7b12
Previously the game ran indefinitely and relied entirely on max_turns_per_episode to end an episode. Add start/food energy values so an episode ends on its own when energy runs out, and mention retro-gamer plot in the lab's training log instructions.
104 lines
1.7 KiB
Markdown
104 lines
1.7 KiB
Markdown
# Forager Training Log
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Document each training attempt below. For each attempt, write your hypothesis
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before you run the experiment, then fill in the evidence and analysis after.
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Use `retro-gamer info runs/forager/` to see a summary of your run,
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`cat runs/forager/training.log` to see the full log, and
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`retro-gamer plot runs/forager/ -o runs/forager/training.png` to graph it.
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---
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## Attempt 1
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### Hypothesis
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*Before training, predict what will happen with the default configuration.
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Will the agent learn to find the food? How quickly? What might go wrong?*
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Your prediction:
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### Configuration
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*Copy the relevant sections of `runs/forager/config.toml` here.*
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```toml
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```
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### Evidence
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*Paste the first and last few lines of your training log, and any interesting
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moments in between.*
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```
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```
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### Analysis
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*What happened? How do the numbers — avg_reward, avg_steps, epsilon, avg_loss —
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tell the story of what the agent learned? Did the result match your prediction?*
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---
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## Attempt 2
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### Hypothesis
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*Based on what you observed in Attempt 1, what will you change and why?
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Predict the outcome.*
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### Configuration
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```toml
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```
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### Evidence
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```
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```
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### Analysis
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---
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## Attempt 3 (if needed)
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### Hypothesis
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### Configuration
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```toml
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```
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### Evidence
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```
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```
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### Analysis
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---
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## Final analysis
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**Which attempt produced the best-trained agent? Run `retro-gamer play` on your
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best run's checkpoints and describe what the agent does.**
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*Your answer:*
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**Compare two of your attempts. What changed between them, and how did that
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change affect the training curve?**
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*Your answer:*
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**If you had more time, what would you try next to improve the agent further?
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Refer to specific hyperparameters or configuration options.**
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*Your answer:*
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