Datasets:

autorl-org
/

arlbench

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'environment'})

This happened while the csv dataset builder was generating data using

hf://datasets/autorl-org/arlbench/minigrid_door_key_dqn.csv (at revision fe903e35c617b047ac5f4c335aae353720d591ea)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 580, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              run_id: int64
              budget: int64
              performance: double
              hp_config.buffer_batch_size: int64
              hp_config.buffer_prio_sampling: bool
              hp_config.buffer_size: int64
              hp_config.initial_epsilon: double
              hp_config.learning_rate: double
              hp_config.learning_starts: int64
              hp_config.target_epsilon: double
              hp_config.use_target_network: bool
              hp_config.buffer_alpha: double
              hp_config.buffer_beta: double
              hp_config.buffer_epsilon: double
              hp_config.target_update_interval: double
              seed: int64
              environment: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2739
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'run_id': Value(dtype='int64', id=None), 'budget': Value(dtype='int64', id=None), 'performance': Value(dtype='float64', id=None), 'hp_config.buffer_batch_size': Value(dtype='int64', id=None), 'hp_config.buffer_prio_sampling': Value(dtype='bool', id=None), 'hp_config.buffer_size': Value(dtype='int64', id=None), 'hp_config.initial_epsilon': Value(dtype='float64', id=None), 'hp_config.learning_rate': Value(dtype='float64', id=None), 'hp_config.learning_starts': Value(dtype='int64', id=None), 'hp_config.target_epsilon': Value(dtype='float64', id=None), 'hp_config.use_target_network': Value(dtype='bool', id=None), 'hp_config.buffer_alpha': Value(dtype='float64', id=None), 'hp_config.buffer_beta': Value(dtype='float64', id=None), 'hp_config.buffer_epsilon': Value(dtype='float64', id=None), 'hp_config.target_update_interval': Value(dtype='float64', id=None), 'seed': Value(dtype='int64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1392, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1041, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 999, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1740, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'environment'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/autorl-org/arlbench/minigrid_door_key_dqn.csv (at revision fe903e35c617b047ac5f4c335aae353720d591ea)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Unnamed: 0 int64	run_id int64	budget int64	performance float64	hp_config.buffer_batch_size int64	hp_config.buffer_prio_sampling bool	hp_config.buffer_size int64	hp_config.initial_epsilon float64	hp_config.learning_rate float64	hp_config.learning_starts int64	hp_config.target_epsilon float64	hp_config.use_target_network bool	hp_config.buffer_alpha float64	hp_config.buffer_beta float64	hp_config.buffer_epsilon float64	hp_config.target_update_interval float64	seed int64
0	0	10,000,000	2,000	32	false	603,170	0.772442	0.000131	21,527	0.08808	false	null	null	null	null	6
1	1	10,000,000	6,929.6875	32	false	71,987	0.543565	0.000001	27,455	0.155853	false	null	null	null	null	6
2	2	10,000,000	953.125	16	false	144,230	0.972334	0.000407	14,187	0.053647	false	null	null	null	null	6
3	3	10,000,000	15,578.125	32	false	943,806	0.84091	0.000063	14,897	0.139829	true	null	null	null	258	6
4	4	10,000,000	2,000	16	true	570,637	0.719301	0.087472	4,263	0.042566	true	0.656577	0.260759	0.000007	489	6
5	5	10,000,000	6,460.9375	16	true	656,682	0.569091	0.00001	12,729	0.164378	true	0.839565	0.105137	0.000805	938	6
6	6	10,000,000	13,390.625	64	false	739,531	0.519594	0.000026	4,839	0.059932	true	null	null	null	1,385	6
7	7	10,000,000	984.375	32	true	523,736	0.54697	0.000758	30,524	0.064395	false	0.14048	0.719164	0.000001	null	6
8	8	10,000,000	1,218.75	32	true	829,116	0.502348	0.00245	9,595	0.147304	false	0.256266	0.580396	0.000023	null	6
9	9	10,000,000	0	16	false	447,691	0.923204	0.003143	10,466	0.162946	true	null	null	null	1,386	6
10	10	10,000,000	16,445.312	64	false	956,129	0.821995	0.000132	20,273	0.004819	true	null	null	null	858	6
11	11	10,000,000	9,640.625	16	true	570,405	0.795436	0.000744	21,759	0.130769	true	0.897581	0.373886	0.000006	1,784	6
12	12	10,000,000	0	64	false	101,148	0.959741	0.003726	32,732	0.03074	false	null	null	null	null	6
13	13	10,000,000	1,000	64	false	407,790	0.534583	0.00307	15,421	0.144689	false	null	null	null	null	6
14	14	10,000,000	8,289.0625	64	true	521,527	0.527169	0.00001	1,611	0.158946	true	0.351898	0.9288	0.000066	64	6
15	15	10,000,000	0	16	false	577,662	0.618946	0.046889	20,514	0.107591	false	null	null	null	null	6
16	16	10,000,000	18,234.375	16	false	739,818	0.745229	0.000014	9,098	0.012548	true	null	null	null	360	6
17	17	10,000,000	2,281.25	16	true	679,721	0.726848	0.000482	29,488	0.198077	true	0.666447	0.270689	0	1,517	6
18	18	10,000,000	2,953.125	16	true	588,739	0.915524	0.001396	28,726	0.055435	false	0.19378	0.953264	0.000056	null	6
19	19	10,000,000	2,039.0625	64	false	254,705	0.606656	0.00039	1,838	0.042287	true	null	null	null	1,174	6
20	20	10,000,000	4,000	64	true	517,873	0.566034	0.00384	13,596	0.113519	true	0.153399	0.493176	0.000003	1,881	6
21	21	10,000,000	976.5625	64	false	903,819	0.541711	0.000577	19,578	0.192425	true	null	null	null	1,860	6
22	22	10,000,000	6,273.4375	64	false	282,465	0.793205	0.000002	16,440	0.195522	false	null	null	null	null	6
23	23	10,000,000	11,289.0625	64	false	630,826	0.937144	0.000029	27,973	0.123957	true	null	null	null	957	6
24	24	10,000,000	2,000	32	false	369,231	0.56845	0.0129	7,050	0.102752	true	null	null	null	1,922	6
25	25	10,000,000	6,304.6875	64	false	333,828	0.540551	0.000109	8,396	0.027365	true	null	null	null	294	6
26	26	10,000,000	0	16	true	672,384	0.622684	0.000127	18,717	0.17225	false	0.277625	0.140168	0	null	6
27	27	10,000,000	2,664.0625	16	true	683,606	0.847813	0.000026	13,084	0.037049	false	0.06628	0.700027	0.00013	null	6
28	28	10,000,000	2,531.25	16	true	588,022	0.636411	0.000071	7,279	0.092511	true	0.801798	0.086187	0.000012	614	6
29	29	10,000,000	3,000	32	false	645,933	0.517681	0.000142	17,214	0.107699	false	null	null	null	null	6
30	30	10,000,000	0	16	false	544,273	0.728456	0.025716	15,582	0.145109	true	null	null	null	656	6
31	31	10,000,000	0	64	false	240,798	0.580269	0.009593	31,472	0.09217	false	null	null	null	null	6
32	32	10,000,000	593.75	64	false	815,713	0.579707	0.001395	13,672	0.01348	true	null	null	null	1,918	6
33	33	10,000,000	0	32	true	17,335	0.592616	0.000101	30,524	0.020823	false	0.870794	0.459621	0.000002	null	6
34	34	10,000,000	14,578.125	32	true	16,614	0.714398	0.000002	9,021	0.045011	true	0.139745	0.021916	0	1,237	6
35	35	10,000,000	3,296.875	64	false	409,659	0.581477	0.001562	16,588	0.197893	true	null	null	null	1,326	6
36	36	10,000,000	0	16	false	517,803	0.712044	0.000594	10,136	0.141608	true	null	null	null	93	6
37	37	10,000,000	1,000	16	true	815,156	0.992746	0.069961	29,751	0.060015	false	0.256926	0.114847	0.000637	null	6
38	38	10,000,000	3,000	64	true	730,985	0.94086	0.000023	13,057	0.075485	false	0.245429	0.180135	0.000006	null	6
39	39	10,000,000	1,000	64	true	502,899	0.971292	0.001479	28,556	0.188102	false	0.702579	0.968286	0.00095	null	6
40	40	10,000,000	15,132.8125	16	true	153,222	0.708743	0.000005	20,201	0.077179	false	0.968117	0.551416	0.000001	null	6
41	41	10,000,000	0	64	true	552,537	0.635826	0.000189	13,776	0.050434	false	0.317277	0.379305	0.000013	null	6
42	42	10,000,000	17,835.938	32	false	862,460	0.524345	0.000019	15,186	0.021821	true	null	null	null	1,422	6
43	43	10,000,000	0	16	true	676,574	0.939617	0.000523	9,998	0.007017	false	0.017805	0.378952	0.000013	null	6
44	44	10,000,000	1,765.625	16	true	25,312	0.671305	0.001292	9,883	0.04274	true	0.581369	0.698317	0.000049	1,898	6
45	45	10,000,000	0	16	false	600,802	0.79437	0.065141	1,559	0.1396	false	null	null	null	null	6
46	46	10,000,000	4,593.75	32	false	694,270	0.545443	0.000014	14,049	0.125036	false	null	null	null	null	6
47	47	10,000,000	0	32	false	541,850	0.961653	0.014109	31,762	0.184037	true	null	null	null	601	6
48	48	10,000,000	10,906.25	16	false	446,961	0.953938	0.000006	22,011	0.088612	true	null	null	null	120	6
49	49	10,000,000	2,000	16	false	740,151	0.949031	0.002306	17,815	0.061585	false	null	null	null	null	6
50	50	10,000,000	0	16	true	680,676	0.531604	0.001087	16,186	0.057516	true	0.519368	0.374248	0.000007	675	6
51	51	10,000,000	1,492.1875	64	true	97,728	0.671696	0.000902	21,949	0.080054	false	0.358374	0.724193	0.000036	null	6
52	52	10,000,000	22,421.875	64	false	764,803	0.849124	0.000048	5,712	0.013465	true	null	null	null	1,918	6
53	53	10,000,000	0	16	true	590,115	0.872699	0.017408	30,731	0.196702	true	0.386532	0.156331	0.000055	1,314	6
54	54	10,000,000	1,218.75	64	true	498,291	0.790541	0.000016	6,389	0.172057	true	0.475915	0.124676	0.000007	1,960	6
55	55	10,000,000	13,289.0625	32	false	118,219	0.635626	0.000104	13,716	0.134605	true	null	null	null	864	6
56	56	10,000,000	0	32	true	822,589	0.826711	0.004283	18,068	0.022985	true	0.41132	0.327833	0	1,475	6
57	57	10,000,000	507.8125	16	false	703,522	0.817393	0.062476	4,303	0.173566	true	null	null	null	731	6
58	58	10,000,000	16,335.9375	16	true	518,643	0.921388	0.000073	8,098	0.017026	true	0.229182	0.109014	0.000001	133	6
59	59	10,000,000	2,000	16	false	162,978	0.779841	0.007367	15,512	0.03152	true	null	null	null	1,563	6
60	60	10,000,000	0	64	false	29,946	0.947846	0.000092	28,907	0.138466	false	null	null	null	null	6
61	61	10,000,000	22,570.312	32	true	496,486	0.84087	0.000024	17,670	0.024359	true	0.056338	0.971024	0	358	6
62	62	10,000,000	2,000	32	true	882,018	0.85981	0.067913	17,138	0.06078	false	0.931511	0.525554	0.000001	null	6
63	63	10,000,000	0	32	true	248,455	0.659117	0.019674	15,579	0.089473	true	0.881871	0.945577	0.000928	754	6
64	64	10,000,000	9,195.3125	64	false	676,021	0.622445	0.000012	6,295	0.184629	true	null	null	null	1,689	6
65	65	10,000,000	12,406.25	16	true	843,016	0.909017	0.000003	5,988	0.061536	true	0.430416	0.116542	0.000019	494	6
66	66	10,000,000	25,039.062	32	true	975,909	0.966281	0.000091	8,711	0.050829	true	0.049593	0.643308	0.000004	755	6
67	67	10,000,000	0	64	false	954,381	0.675968	0.030741	25,466	0.072128	false	null	null	null	null	6
68	68	10,000,000	3,359.375	16	true	745,494	0.763454	0.000274	1,041	0.085655	true	0.216171	0.93307	0.000001	1,717	6
69	69	10,000,000	3,429.6875	64	true	606,116	0.557831	0.00436	21,260	0.162576	true	0.915714	0.058855	0.000001	1,431	6
70	70	10,000,000	5,281.25	32	true	108,125	0.90867	0.000232	29,032	0.146925	true	0.379776	0.520482	0.00036	1,475	6
71	71	10,000,000	23,492.188	16	false	919,590	0.855228	0.000008	16,373	0.028923	true	null	null	null	680	6
72	72	10,000,000	0	32	false	148,674	0.628458	0.023323	16,639	0.179893	true	null	null	null	894	6
73	73	10,000,000	2,476.5625	32	true	915,058	0.865872	0.004342	10,227	0.115964	false	0.797634	0.351085	0.000121	null	6
74	74	10,000,000	2,687.5	16	false	441,893	0.743205	0.000175	19,050	0.124613	true	null	null	null	834	6
75	75	10,000,000	1,492.1875	64	true	212,262	0.529692	0.023996	30,183	0.024904	true	0.183618	0.124739	0.000398	114	6
76	76	10,000,000	7,429.6875	64	true	863,611	0.783253	0.000069	11,891	0.151715	true	0.660746	0.522153	0.000009	1,803	6
77	77	10,000,000	1,000	32	false	725,855	0.519279	0.007338	7,908	0.180727	true	null	null	null	1,641	6
78	78	10,000,000	20,484.375	16	true	330,953	0.90694	0.000005	8,241	0.014702	false	0.401281	0.317732	0.000075	null	6
79	79	10,000,000	18,015.625	64	false	218,464	0.986909	0.000006	10,256	0.036779	true	null	null	null	1,779	6
80	80	10,000,000	2,000	16	false	112,405	0.729485	0.000041	11,071	0.097034	false	null	null	null	null	6
81	81	10,000,000	2,000	64	false	999,809	0.59863	0.000473	10,237	0.061531	false	null	null	null	null	6
82	82	10,000,000	12,835.9375	64	true	873,962	0.916991	0.000012	25,506	0.003422	true	0.237272	0.511794	0.000089	196	6
83	83	10,000,000	1,000	32	false	229,436	0.838571	0.000921	1,343	0.095689	false	null	null	null	null	6
84	84	10,000,000	20,859.375	16	false	582,748	0.553736	0.000027	15,522	0.005169	true	null	null	null	1,507	6
85	85	10,000,000	507.8125	16	false	639,991	0.97427	0.007787	27,954	0.098594	true	null	null	null	137	6
86	86	10,000,000	4,046.875	64	false	719,677	0.674996	0.000019	9,446	0.026332	false	null	null	null	null	6
87	87	10,000,000	593.75	16	true	652,481	0.926623	0.000238	31,791	0.053861	true	0.488915	0.263553	0.000197	466	6
88	88	10,000,000	3,000	16	false	715,435	0.779026	0.003348	14,313	0.002057	true	null	null	null	1,932	6
89	89	10,000,000	8,875	64	true	1,687	0.970834	0.000025	6,925	0.13861	true	0.272003	0.975344	0.000036	1,042	6
90	90	10,000,000	3,953.125	32	false	141,837	0.983669	0.020212	20,631	0.009538	false	null	null	null	null	6
91	91	10,000,000	11,132.8125	64	false	101,173	0.879492	0.000001	31,723	0.123397	false	null	null	null	null	6
92	92	10,000,000	7,992.1875	64	false	519,482	0.533037	0.00023	14,936	0.041356	true	null	null	null	526	6
93	93	10,000,000	765.625	32	true	906,328	0.908182	0.000579	28,064	0.192517	true	0.634523	0.998014	0.000894	1,207	6
94	94	10,000,000	0	16	false	3,086	0.599456	0.060341	11,513	0.12804	true	null	null	null	1,584	6
95	95	10,000,000	4,796.875	16	true	222,015	0.841863	0.000003	13,627	0.056053	false	0.356399	0.709346	0	null	6
96	96	10,000,000	7,007.8125	16	true	800,460	0.977784	0.000038	27,270	0.021694	false	0.753522	0.164418	0.000005	null	6
97	97	10,000,000	507.8125	16	true	591,005	0.717766	0.00985	30,339	0.060532	true	0.491409	0.59227	0.000862	1,395	6
98	98	10,000,000	0	32	true	944,683	0.567774	0.003993	30,400	0.133268	true	0.207001	0.373801	0.000067	1,300	6
99	99	10,000,000	257.8125	64	false	816,339	0.955725	0.000024	12,754	0.076599	false	null	null	null	null	6

End of preview.

The ARLBench Performance Dataset

ARLBench is a benchmark for hyperparameter optimization in Reinforcement Learning. Since we performed several thousand runs on the benchmark to find meaningful HPO test settings in RL, we collect them in this dataset for future use. These runs could be used to meta-learn information about the hyperparameter landscape or warmstart HPO tools.

In detail, it contains each 10 runs for PPO, DQN and SAC respectively on the Atari-5 environments, four XLand gridworlds, four Brax walkers, five classic control and two Box2D environments. For more information, refer to the ARLBench paper.

Downloads last month: 5

Edit dataset card

Size of downloaded dataset files:

3.22 MB

Size of the auto-converted Parquet files:

750 kB

Number of rows:

20,480