| --- |
| 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. |
