Datasets:

aditijc
/

snooker-testbed-phase4f2-longer-v1

step int64 100k 1.5M	curriculum_stage int64 2 2	mean_score float64 0.06 5.69	max_score float64 1 23	mean_shots float64 200 200	mean_efficiency float64 0 0.03	mean_foul_rate float64 0.94 1	episodes int64 16 16
100,000	2	0.5625	7	200	0.002813	0.990625	16
200,000	2	0.0625	1	200	0.000313	0.999063	16
300,000	2	3	14	200	0.015	0.969063	16
400,000	2	0.9375	7	200	0.004688	0.9675	16
500,000	2	3.4375	13	200	0.017188	0.965937	16
600,000	2	2.6875	10	200	0.013438	0.946875	16
700,000	2	2.5625	10	200	0.012813	0.96	16
800,000	2	3.875	15	200	0.019375	0.944063	16
900,000	2	1.9375	12	200	0.009688	0.9625	16
1,000,000	2	4	15	200	0.02	0.949375	16
1,100,000	2	4.25	13	200	0.02125	0.950625	16
1,200,000	2	4.75	16	200	0.02375	0.939375	16
1,300,000	2	5.625	17	200	0.028125	0.950625	16
1,400,000	2	4.4375	15	200	0.022188	0.949062	16
1,500,000	2	5.6875	23	200	0.028438	0.945625	16

snooker-testbed-phase4f2-longer-v1

Phase 4F2: extends 4F's 500k-step breakthrough to 1.5M steps. Job 7316111 on torch h200 gh112, 52m18s wall, ran 2026-04-27 14:14-15:06 UTC. SUSTAINED IMPROVEMENT THROUGHOUT TRAINING — trajectory does NOT plateau, continues climbing past 4F's 500k endpoint. Mean over 15 evals: 3.19 (vs 4F's 1.25, 4G's 0.76, random 2.10). Peak score 5.69 at step 1.5M (2.7x random!). Single-ep max 23 points (highest of any run). Foul rate broke 95% multiple times: low 93.9% at step 1.2M (vs 4F's 96.4%). Same recipe as 4F: PPO + MultiDiscrete([36,8,5,5]) + 79-D augmented obs + curriculum [0,1,2,3,4]. The action discretization breakthrough scales with training time.

Dataset Info

Rows: 15
Columns: 8

Columns

Column	Type	Description
step	Value('int64')	Global PPO timestep (100k-1.5M)
curriculum_stage	Value('int64')	No description provided
mean_score	Value('float64')	Mean over 16 eps. CLIMBED throughout training. Peak 5.69 at step 1.5M.
max_score	Value('float64')	Best single-ep score (peak 23 at step 1.5M — highest of any run)
mean_shots	Value('float64')	No description provided
mean_efficiency	Value('float64')	No description provided
mean_foul_rate	Value('float64')	Sustained drop 99% → 93.9% across training (vs 4F best 96.4%)
episodes	Value('int64')	No description provided

Generation Parameters

{
  "script_name": "sbatch/train_sim.sbatch (Phase 4F2 \u2014 discrete actions, 1.5M steps)",
  "model": "stable-baselines3 PPO, MlpPolicy net_arch=[256,256], MultiDiscrete([36,8,5,5])",
  "description": "Phase 4F2: extends 4F's 500k-step breakthrough to 1.5M steps. Job 7316111 on torch h200 gh112, 52m18s wall, ran 2026-04-27 14:14-15:06 UTC. SUSTAINED IMPROVEMENT THROUGHOUT TRAINING \u2014 trajectory does NOT plateau, continues climbing past 4F's 500k endpoint. Mean over 15 evals: 3.19 (vs 4F's 1.25, 4G's 0.76, random 2.10). Peak score 5.69 at step 1.5M (2.7x random!). Single-ep max 23 points (highest of any run). Foul rate broke 95% multiple times: low 93.9% at step 1.2M (vs 4F's 96.4%). Same recipe as 4F: PPO + MultiDiscrete([36,8,5,5]) + 79-D augmented obs + curriculum [0,1,2,3,4]. The action discretization breakthrough scales with training time.",
  "hyperparameters": {
    "algorithm": "PPO",
    "total_timesteps": 1500000,
    "n_envs": 8,
    "n_steps": 512,
    "batch_size": 2048,
    "ent_coef": 0.01,
    "learning_rate": 0.0003,
    "gamma": 0.99,
    "discrete_actions": true,
    "discrete_phi_bins": 36,
    "discrete_force_bins": 8,
    "discrete_spin_bins": 5,
    "obs_dim": 79,
    "code_version": "2026-04-27-phase4f-discrete",
    "commit": "62b5567"
  },
  "input_datasets": [],
  "experiment_name": "snooker-testbed",
  "job_id": "torch:7316111",
  "cluster": "torch",
  "artifact_status": "final",
  "canary": false
}

Experiment Documentation

For complete experiment details, see https://github.com/aditijc/snooker-testbed

Usage

from datasets import load_dataset

dataset = load_dataset("aditijc/snooker-testbed-phase4f2-longer-v1", split="train")
print(f"Loaded {len(dataset)} rows")

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Total file size:

6.42 MB