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license: mit
language:
- en
pretty_name: CE-CM Precomputed
---
# Precomputed Experiment Artifacts
This dataset contains precomputed data used to reproduce cached experiments, planner behavior, and paper plots without rerunning expensive planning/training jobs.
## Contents
```text
precompute/
exp_policies/
level1-lot.npy
...
rollouts/
level1-lot.npy
...
diverse_rollouts/
level1-lot_0.35.npy
...
trees/
level1-lot.npy
...
plans/
level1-lot-1.npy
...
```
## Files
exp_policies/*.npy
Precomputed Q-table policies for each level. These are used by the baseline agent and as action priors for MCTS planning.
rollouts/*.npy
Cached simulated trajectories for sampled capability vectors. These are used by CachedLearner when running standard cached capability inference.
diverse_rollouts/*.npy
Cached sets of diverse simulated trajectories for sampled capability vectors. Filenames include the diversity threshold, for example:
level1-lot_0.35.npy. These are used by CachedLearner(diverse=True).
trees/*.npy
Cached state-transition trees for sampled capability vectors. These are mainly used for trajectory/tree validation and analysis.
plans/*.npy
Ground-truth planner rollouts used by experiment and plotting code. Filenames follow:
<level-name>-<agent-type>.npy
For example:
level1-lot-2.npy
## How The Caches Are Used
Cached inference avoids rerunning MCTS simulations for every sampled capability vector. Instead, the learner loads simulated trajectories from rollouts/ or diverse_rollouts/ and compares them against observed trajectories.
The planner policies in ``exp_policies/`` are required by the baseline agent and by MCTS when using precomputed Q-value action biases.
The plans/ files store reference trajectories and action plans generated under ground-truth capabilities. They are used by experiment scripts and plotting notebooks when comparing inferred/baseline behavior to ground truth.
## Regenerating
These artifacts can be regenerated from the repository, but doing so may be expensive.
./get_rollouts.sh
./get_diverse_rollouts.sh
./get_trees.sh
Q-table policies can be trained with:
uv run python precompute/q_table.py --map-file level1-lot.txt --total-timesteps 100000 --q-default 0.01
Use the exact same environment version as the paper-release branch when regenerating artifacts. |