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metadata 
license: cc-by-4.0
pretty_name: >-
  PPO Hyperparameter Corpus — From Importance Shifts to Landscape Topology (PPSN
  2026)
tags:
  - reinforcement-learning
  - PPO
  - hyperparameter-optimization
  - fitness-landscape-analysis
  - parallel-rl
  - automl
  - fanova
  - continuous-control
size_categories:
  - 10K<n<100K
task_categories:
  - tabular-regression
configs:
  - config_name: configs_with_hyperparameters
    data_files: data/configs_with_hyperparameters.csv
    default: true
  - config_name: all_runs
    data_files: data/all_runs.parquet
  - config_name: eval_trajectories
    data_files: data/eval_trajectories.parquet

Data Release — From Importance Shifts to Landscape Topology: Characterizing and Exploiting Hyperparameter Spaces in Parallel RL

Authors: Yingjie Zou, Zhong Fan (University of Exeter, Exeter, UK) Venue: PPSN 2026 (Parallel Problem Solving from Nature) License: CC-BY-4.0

This HuggingFace dataset is the data and figure release accompanying the paper. It provides the full per-run hyperparameter (HP) corpus, the validated analysis tables that back every figure and table in the paper, the final paper figures, and the compiled appendix (supplement.pdf).

Corpus

The corpus consists of 37,200 PPO training runs organised as:

  • 5 chapters (HP groups): Chap1 (PPO core), Chap2 (GAE), Chap3 (normalization), Chap4 (optimization), Chap5 (architecture)
  • × 4 Brax/MuJoCo continuous-control environments: ant, halfcheetah, hopper, walker2d
  • × 5 parallelism levels: num_envs ∈ {64, 128, 256, 512, 1024}
  • × 3 seeds: {42, 43, 44}

Per-chapter configuration counts: Chap1/2/4/5 = 135 configs (8,100 runs each); Chap3 = 80 configs (4,800 runs). Total = 37,200 runs. Every run completed (state == finished) with a non-missing final_eval_return. The final performance metric is eval/episode_returns (W&B summary), stored as final_eval_return.

Training used the evorl PPO implementation on NVIDIA GH200 (Grace Hopper) GPUs via SLURM. The 37,200-run total reconciles exactly with the upstream training-run logs (Chap1/2/4/5 = 8,100 runs each, Chap3 = 4,800).

Package layout 

04_huggingface/
├── README.md                              # this dataset card
├── .gitattributes                          # git-LFS tracking (*.parquet)
├── CHECKSUMS.sha256                        # sha256 of every released data/figure/document file
├── supplement.pdf                          # compiled appendix (supplementary material) PDF
├── data/
│   ├── configs_with_hyperparameters.csv   # headline per-run table (37,200 × 23)
│   ├── all_runs.parquet                   # full consolidated corpus (37,200 × 48)
│   ├── eval_trajectories.parquet          # eval-return trajectories (run × 10 snapshots)
│   └── derived/                           # validated analysis tables (back the paper)
└── figures/                               # final main + supplement paper figures (PDF)

configs_with_hyperparameters.csv — column dictionary

One row per run; 37,200 rows × 23 columns. The config. prefix has been dropped from HP names for readability; names follow data/derived/chapter_hp_mapping.md.

Column Type Description
chapter str HP group / chapter: Chap1–Chap5
env_name str Brax/MuJoCo env: ant, halfcheetah, hopper, walker2d
num_envs int Parallelism level (number of parallel envs): 64–1024
seed int Random seed: 42, 43, 44
rollout_length int PPO rollout length (steps per env per update)
discount float Discount factor γ
gae_lambda float GAE λ
gae_horizon int GAE horizon (0 = full rollout)
clip_epsilon float PPO clipping ε
loss_weights.actor_loss float Actor (policy) loss weight
loss_weights.critic_loss float Critic (value) loss weight
loss_weights.actor_entropy float Entropy bonus weight (negative = bonus)
optimizer.lr float Adam learning rate
optimizer.grad_clip_norm float Global gradient-norm clip (0 = off)
normalize_obs bool Observation normalization on/off
normalize_gae bool Advantage (GAE) normalization on/off
minibatch_size int SGD minibatch size
reuse_rollout_epochs int PPO epochs per rollout (sample reuse)
agent_network.actor_hidden_layer_sizes str Actor MLP hidden sizes, e.g. [256, 256]
agent_network.critic_hidden_layer_sizes str Critic MLP hidden sizes
total_timesteps int Training budget in env steps (fixed constant; see note below)
final_eval_return float Outcome: final evaluation episodic return
eval_episode_lengths float Final evaluation episode length (auxiliary)

Note on total_timesteps: this is a fixed training-budget constant (10,485,760 ≈ 1×10^7) shared by every one of the 37,200 runs. It is therefore not a swept hyperparameter and is not part of the per-chapter swept-HP grid documented in data/derived/chapter_hp_mapping.md; it is retained in this table to fully specify each run's training budget.

Within each chapter, a subset of these HPs is swept and the rest are fixed; see data/derived/chapter_hp_mapping.md for the per-chapter swept/fixed split and grids.

File → paper artifact mapping

Released file Backs
data/configs_with_hyperparameters.csv Headline per-run corpus; input to all fANOVA / landscape / HPO analyses
data/all_runs.parquet Full consolidated corpus (48 cols incl. training/eval metrics)
data/eval_trajectories.parquet Per-run eval-return trajectories (10 snapshots/run); stage-wise analysis input
data/derived/fanova_individual_importance.csv Table 3; Fig S1 (figS_A1_fanova_heatmap), Fig S4 (figS_A4_importance_shift_ratio) — HP importance shifts
data/derived/fanova_pairwise_importance.csv Pairwise fANOVA importance (HP interaction analysis)
data/derived/stagewise_importance.csv §4.2 stage-wise importance; Fig S3 (figS_A3_stagewise_evolution)
data/derived/graphfla_landscape_features_validated.csv §5 landscape; Table 4; Fig S5 (figS_A5_graphfla_feature_trends), Fig S6 (figS_A6_landscape_profile), Fig S8 (figS_A8_feature_method_corr)
data/derived/hpo_results_v2.csv §6 HPO benchmark; Fig 3 (fig_main_results); Fig S7 (figS_A7_hpo_benchmark), Fig S8
data/derived/selector_n100_perf.csv §S5 selector study (n=100 per-seed performance); Fig S11 (figS_A14_selector_n100_accuracy)
data/derived/selector_n100_robustness.json §S5 selector robustness (10 optimiser-seed headline numbers); Fig S11
data/derived/robust_trends_R1-8.csv Table S2; block-structure-robust trend tests; ρ* labels in Fig S5
data/derived/mayor_metrics_v2_all.csv §S6 network diagnostics; Fig S12 (figS_mayor_metrics)
data/derived/seed_reliability.csv Seed-reliability validation (cross-seed agreement)
data/derived/discretization_validation.csv Grid-discretization validation
data/derived/chapter_hp_mapping.md Per-chapter swept/fixed HP dictionary and grids
figures/fig_main_results.pdf Main: HPO benchmark results
figures/fig_landscape_trends.pdf Main: landscape-feature trends vs parallelism
figures/fig_stagewise.pdf Main: stage-wise importance
figures/fig_importance_shift.pdf Main: importance shift vs parallelism
figures/figS_*.pdf Supplement figures (S1–S12); see MANIFEST notes in the supplement
supplement.pdf Compiled supplementary material (appendix): full text of §S1–S6 and Figures S1–S12

Note on figure numbering: supplement figure files are named by their internal label (figS_A1…figS_A14, figS_mayor_metrics); the paper text refers to them as Figures S1–S12 in order. The mapping above gives both for the key analysis figures.

§S6 (network diagnostics) was corrected for the camera-ready to use mayor_metrics_v2_all.csv. The earlier per-step plasticity CSVs are superseded and are intentionally not part of this release.

Regenerating the figures

  • Main figures (fig_main_results, fig_landscape_trends, fig_stagewise, fig_importance_shift): EXP/analysis_scripts/build_main_figures.py
  • Supplement figures (figS_*): supplement/_make_supplement_figs.py

Both scripts read the derived CSVs released here (and the consolidated parquet) and emit the vector PDFs in figures/. The analysis tables themselves are produced by the scripts in EXP/analysis_scripts/ (run_fanova.py, run_graphfla.py, run_hpo_benchmark.py, run_stagewise_fanova.py, run_selector_n100.py, compute_robust_trends.py, finalize_validated_graphfla.py) from all_runs.parquet / configs_with_hyperparameters.csv. These analysis/figure scripts are part of the paper's code repository and are not bundled in this data release.

Integrity

  • CHECKSUMS.sha256 lists the sha256 of every released data, figure, and document file. eval_trajectories.parquet sha256 = e7ef8116687e4bee178f04800c649ac35b03bc1806fe66cd5b175f5f2b70cb1f (matches the project DATA_PROVENANCE.md).

Reproducibility & data availability

All derived quantities in the paper and supplement are computed from the raw corpus (data/all_runs.parquet, data/eval_trajectories.parquet; 37,200 PPO runs across 5 HP chapters, 4 Brax environments, 5 parallelism levels, and 3 seeds) using the analysis scripts provided with the paper. Per-file source hashes are in CHECKSUMS.sha256; full regeneration paths are recorded in the project's DATA_PROVENANCE.md.

Citation

Please cite the PPSN 2026 paper. License: CC-BY-4.0.