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
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: -.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.