Dataset Preview
Full Screen Viewer
Full Screen
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<0: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 1: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 2: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 3: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 4: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 5: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 6: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 7: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 8: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 9: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 10: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 11: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 12: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 13: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>>
to
{'0': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '1': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '2': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '3': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '4': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '5': {'type': Value(dtype='string', id=None), 'sentence-1-token-indic
...
), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '6': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '7': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '8': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '9': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '10': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<0: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 1: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 2: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 3: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 4: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 5: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 6: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 7: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 8: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 9: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 10: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 11: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 12: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>, 13: struct<type: string, sentence-1-token-indices: list<item: int64>, sentence-2-token-indices: list<item: int64>, intention: string>>
to
{'0': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '1': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '2': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '3': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '4': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '5': {'type': Value(dtype='string', id=None), 'sentence-1-token-indic
...
), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '6': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '7': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '8': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '9': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}, '10': {'type': Value(dtype='string', id=None), 'sentence-1-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'sentence-2-token-indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'intention': Value(dtype='string', id=None)}}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1524, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
sentence-pair-index
int64 | page-sentence-1
int64 | type-sentence-1
string | id-sentence-1
string | num_section-sentence-1
int64 | page-sentence-2
int64 | type-sentence-2
string | id-sentence-2
string | num_section-sentence-2
int64 | text-sentence-1
string | text-sentence-2
string | edits-combination
dict | id_version_1
string | id_version_2
string | sentence_pair_id
string | num_paragraph-sentence-1
int64 | num_sentence-sentence-1
int64 | num_paragraph-sentence-2
int64 | num_sentence-sentence-2
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | Title | Y6LzLWXlS8E_00_00_00 | 0 | 0 | Title | WNev_iSes_00_00_00 | 0 | Deconfounded Imitation Learning | Deconfounded Imitation Learning | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00000 | null | null | null | null |
1 | 0 | Paragraph | Y6LzLWXlS8E_01_00_00 | 1 | null | null | null | null | Anonymous Author(s) | {
"0": {
"type": "Deletion",
"sentence-1-token-indices": [
0,
19
],
"sentence-2-token-indices": null,
"intention": "Content"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00001 | 0 | 0 | null | null |
|
2 | 0 | Paragraph | Y6LzLWXlS8E_01_01_00 | 1 | null | null | null | null | AffiliationAddress email | {
"0": {
"type": "Deletion",
"sentence-1-token-indices": [
0,
24
],
"sentence-2-token-indices": null,
"intention": "Content"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00002 | 1 | 0 | null | null |
|
3 | 0 | Abstract | Y6LzLWXlS8E_01_02_00 | 1 | 0 | Abstract | WNev_iSes_01_00_00 | 1 | Abstract | Abstract | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00003 | 2 | 0 | 0 | 0 |
4 | 0 | Abstract | Y6LzLWXlS8E_01_03_00 | 1 | 0 | Abstract | WNev_iSes_01_01_00 | 1 | Standard imitation learning can fail when the expert demonstrators have differentsensory inputs than the imitating agent. | Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
72,
88
],
"sentence-2-token-indices": [
72,
89
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00004 | 3 | 0 | 1 | 0 |
5 | 0 | Abstract | Y6LzLWXlS8E_01_03_01 | 1 | 0 | Abstract | WNev_iSes_01_01_01 | 1 | This partial observability gives rise tohidden confounders in the causal graph, which lead to the failure to imitate. | This is because partial observability gives rise to hidden confounders in the causal graph. | {
"0": {
"type": "Insertion",
"sentence-1-token-indices": null,
"sentence-2-token-indices": [
5,
15
],
"intention": "Content"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
39,
47
],
"sentence-2-token-indices": [
49,
58
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
74,
118
],
"sentence-2-token-indices": [
85,
91
],
"intention": "Content"
},
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00005 | 3 | 1 | 1 | 1 |
6 | 0 | Abstract | Y6LzLWXlS8E_01_03_02 | 1 | 0 | Abstract | WNev_iSes_01_01_02 | 1 | Webreak down the space of confounded imitation learning problems and identify threesettings with different data requirements in which the correct imitation policy canbe identified. | We break down the space of confounded imitation learning problems and identify three settings with different data requirements in which the correct imitation policy can be identified. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
0,
7
],
"sentence-2-token-indices": [
0,
8
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
78,
91
],
"sentence-2-token-indices": [
79,
93
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
163,
168
],
"sentence-2-token-indices": [
165,
171
],
"intention": "Improve-grammar-Typo"
},
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00006 | 3 | 2 | 1 | 2 |
7 | 0 | Abstract | Y6LzLWXlS8E_01_03_03 | 1 | 0 | Abstract | WNev_iSes_01_01_03 | 1 | We then introduce an algorithm for deconfounded imitation learning,which trains an inference model jointly with a latent-conditional policy. | We then introduce an algorithm for deconfounded imitation learning, which trains an inference model jointly with a latent-conditional policy. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
58,
72
],
"sentence-2-token-indices": [
58,
73
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00007 | 3 | 3 | 1 | 3 |
8 | 0 | Abstract | Y6LzLWXlS8E_01_03_04 | 1 | 0 | Abstract | WNev_iSes_01_01_04 | 1 | At testtime, the agent alternates between updating its belief over the latent and actingunder the belief. | At test time, the agent alternates between updating its belief over the latent and acting under the belief. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
3,
12
],
"sentence-2-token-indices": [
3,
13
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
82,
93
],
"sentence-2-token-indices": [
83,
95
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00008 | 3 | 4 | 1 | 4 |
9 | 0 | Abstract | Y6LzLWXlS8E_01_03_05 | 1 | 0 | Abstract | WNev_iSes_01_01_05 | 1 | We show in theory and practice that this algorithm convergesto the correct interventional policy, solves the confounding issue, and can undercertain assumptions achieve an asymptotically optimal imitation performance | We show in theory and practice that this algorithm converges to the correct interventional policy, solves the confounding issue, and can under certain assumptions achieve an asymptotically optimal imitation performance. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
51,
62
],
"sentence-2-token-indices": [
51,
63
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
136,
148
],
"sentence-2-token-indices": [
137,
150
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
205,
216
],
"sentence-2-token-indices": [
207,
219
],
"intention": "Improve-grammar-Typo"
},
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00009 | 3 | 5 | 1 | 5 |
10 | 0 | Section | Y6LzLWXlS8E_02_00_00 | 2 | 0 | Section | WNev_iSes_02_00_00 | 2 | Introduction | Introduction | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00010 | null | null | null | null |
11 | 0 | Paragraph | Y6LzLWXlS8E_02_00_00 | 2 | 0 | Paragraph | WNev_iSes_02_00_00 | 2 | In imitation learning (IL), an agent learns a policy directly from expert demonstrations withoutrequiring the specification of a reward function. | In imitation learning (IL), an agent learns a policy directly from expert demonstrations without requiring the specification of a reward function. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
89,
105
],
"sentence-2-token-indices": [
89,
106
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00011 | 0 | 0 | 0 | 0 |
12 | 0 | Paragraph | Y6LzLWXlS8E_02_00_01 | 2 | 0 | Paragraph | WNev_iSes_02_00_01 | 2 | This paradigm could be essential for solving realworld problems in autonomous driving and robotics where reward functions can be difficult to shapeand online learning may be dangerous. | This paradigm could be essential for solving realworld problems in autonomous driving and robotics where reward functions can be difficult to shape and online learning may be dangerous. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
142,
150
],
"sentence-2-token-indices": [
142,
151
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00012 | 0 | 1 | 0 | 1 |
13 | 0 | Paragraph | Y6LzLWXlS8E_02_00_02 | 2 | 0 | Paragraph | WNev_iSes_02_00_02 | 2 | However, standard IL requires that the conditions under whichthe agent operates exactly match those encountered by the expert. | However, standard IL requires that the conditions under which the agent operates exactly match those encountered by the expert. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
56,
64
],
"sentence-2-token-indices": [
56,
65
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00013 | 0 | 2 | 0 | 2 |
14 | 0 | Paragraph | Y6LzLWXlS8E_02_00_03 | 2 | 0 | Paragraph | WNev_iSes_02_00_03 | 2 | In particular, they assume thatthere are no latent confounders —variables that affect the expert behavior, but that are not observed bythe agent. | In particular, they assume that there are no latent confounders —variables that affect the expert behavior, but that are not observed by the agent. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
27,
36
],
"sentence-2-token-indices": [
27,
37
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
133,
138
],
"sentence-2-token-indices": [
134,
140
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00014 | 0 | 3 | 0 | 3 |
15 | 0 | Paragraph | Y6LzLWXlS8E_02_00_04 | 2 | 0 | Paragraph | WNev_iSes_02_00_04 | 2 | This assumption is often unrealistic. | This assumption is often unrealistic. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00015 | 0 | 4 | 0 | 4 |
16 | 0 | Paragraph | Y6LzLWXlS8E_02_00_05 | 2 | 0 | Paragraph | WNev_iSes_02_00_05 | 2 | Consider a human driver who is aware of the weatherforecast and lowers its speed in icy conditions, even if those are not visible from observations. | Consider a human driver who is aware of the weather forecast and lowers its speed in icy conditions, even if those are not visible from observations. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
44,
59
],
"sentence-2-token-indices": [
44,
60
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00016 | 0 | 5 | 0 | 5 |
17 | 0 | Paragraph | Y6LzLWXlS8E_02_00_06 | 2 | 0 | Paragraph | WNev_iSes_02_00_06 | 2 | Animitator agent without access to the weather forecast will not be able to adapt to such conditions. | An imitator agent without access to the weather forecast will not be able to adapt to such conditions. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
0,
10
],
"sentence-2-token-indices": [
0,
11
],
"intention": "Language"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00017 | 0 | 6 | 0 | 6 |
18 | 0 | Paragraph | Y6LzLWXlS8E_02_01_00 | 2 | 0 | Paragraph | WNev_iSes_02_01_00 | 2 | In such a situation, an imitating agent may take their own past actions as evidence for the values ofthe confounder. | In such a situation, an imitating agent may take their own past actions as evidence for the values of the confounder. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
99,
104
],
"sentence-2-token-indices": [
99,
105
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00018 | 1 | 0 | 1 | 0 |
19 | 0 | Paragraph | Y6LzLWXlS8E_02_01_01 | 2 | 0 | Paragraph | WNev_iSes_02_01_01 | 2 | A self-driving car, for instance, could conclude that it is driving fast, thus there can beno ice on the road. | A self-driving car, for instance, could conclude that it is driving fast, thus there can be no ice on the road. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
89,
93
],
"sentence-2-token-indices": [
89,
94
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00019 | 1 | 1 | 1 | 1 |
20 | 0 | Paragraph | Y6LzLWXlS8E_02_01_02 | 2 | 0 | Paragraph | WNev_iSes_02_01_02 | 2 | This issue of causal delusion was first pointed out in Ortega and Braun [2010a,b]and studied in more depth by Ortega et al. | This issue of causal delusion was first pointed out in Ortega and Braun [2010a,b] and studied in more depth by Ortega et al. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
72,
84
],
"sentence-2-token-indices": [
72,
85
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00020 | 1 | 2 | 1 | 2 |
21 | 0 | Paragraph | Y6LzLWXlS8E_02_01_03 | 2 | 0 | Paragraph | WNev_iSes_02_01_03 | 2 | The authors analyze the causal structure of thisproblem and argue that an imitator needs to learn a policy that corresponds to a certain interventionaldistribution. | The authors analyze the causal structure of this problem and argue that an imitator needs to learn a policy that corresponds to a certain interventional distribution. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
44,
55
],
"sentence-2-token-indices": [
44,
56
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
137,
164
],
"sentence-2-token-indices": [
138,
166
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00021 | 1 | 3 | 1 | 3 |
22 | 0 | Paragraph | Y6LzLWXlS8E_02_01_04 | 2 | 0 | Paragraph | WNev_iSes_02_01_04 | 2 | They then show that the classic DAgger algorithm [Ross et al., 2011], which requiresquerying experts at each time step, solves this problem and converges to the interventional policy. | They then show that the classic DAgger algorithm [Ross et al., 2011], which requires querying experts at each time step, solves this problem and converges to the interventional policy. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
76,
92
],
"sentence-2-token-indices": [
76,
93
],
"intention": "Language"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00022 | 1 | 4 | 1 | 4 |
23 | 0 | Paragraph | Y6LzLWXlS8E_02_02_00 | 2 | 0 | Paragraph | WNev_iSes_02_02_00 | 2 | In this paper, we present a solution to a confounded IL problem, where both the expert policy andthe environment dynamics are Markovian. | In this paper, we present a solution to a confounded IL problem, where both the expert policy and the environment dynamics are Markovian. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
94,
100
],
"sentence-2-token-indices": [
94,
101
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00023 | 2 | 0 | 2 | 0 |
24 | 0 | Paragraph | Y6LzLWXlS8E_02_02_01 | 2 | 0 | Paragraph | WNev_iSes_02_02_01 | 2 | The solution does not require querying experts. | The solution does not require querying experts. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00024 | 2 | 1 | 2 | 1 |
25 | 0 | Paragraph | Y6LzLWXlS8E_02_02_02 | 2 | 0 | Paragraph | WNev_iSes_02_02_02 | 2 | We firstpresent a characterization of confounded IL problems depending on properties of the environmentand expert policy (section 3). | We first present a characterization of confounded IL problems depending on properties of the environment and expert policy (Section 3). | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
3,
15
],
"sentence-2-token-indices": [
3,
16
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
92,
106
],
"sentence-2-token-indices": [
93,
108
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
121,
129
],
"sentence-2-token-indices": [
123,
131
],
"intention": "Improve-grammar-Typo"
},
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00025 | 2 | 2 | 2 | 2 |
26 | 0 | Paragraph | Y6LzLWXlS8E_02_02_03 | 2 | 0 | Paragraph | WNev_iSes_02_02_03 | 2 | We then show theoretically that an imitating agent can learn behaviorsthat approach optimality when the above Markov assumptions and a recurrence property hold. | We then show theoretically that an imitating agent can learn behaviors that approach optimality when the above Markov assumptions and a recurrence property hold. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
61,
74
],
"sentence-2-token-indices": [
61,
75
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00026 | 2 | 3 | 2 | 3 |
27 | 0 | Paragraph | Y6LzLWXlS8E_02_03_00 | 2 | 1 | Paragraph | WNev_iSes_02_03_00 | 2 | We then introduce a practical algorithm for deconfounded imitation learning that does not require expert queries (section 4). | We then introduce a practical algorithm for deconfounded imitation learning that does not require expert queries (Section 4). | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
113,
121
],
"sentence-2-token-indices": [
113,
121
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00027 | 3 | 0 | 3 | 0 |
28 | 0 | Paragraph | Y6LzLWXlS8E_02_03_01 | 2 | 1 | Paragraph | WNev_iSes_02_03_01 | 2 | An agent jointly learns an inference network for the valueof latent variables that explain the environment dynamics as well as a latent-conditional policy. | An agent jointly learns an inference network for the value of latent variables that explain the environment dynamics as well as a latent-conditional policy. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
53,
60
],
"sentence-2-token-indices": [
53,
61
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00028 | 3 | 1 | 3 | 1 |
29 | 0 | Paragraph | Y6LzLWXlS8E_02_04_00 | 2 | 1 | Paragraph | WNev_iSes_02_03_02 | 2 | At test time, the agent iteratively samples latents from its belief, acts in the environ- ment, and updates the belief based on the environment dynamics. | At test time, the agent iteratively samples latents from its belief, acts in the environment, and updates the belief based on the environment dynamics. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
81,
95
],
"sentence-2-token-indices": [
81,
93
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00029 | 4 | 0 | 3 | 2 |
30 | 0 | Paragraph | Y6LzLWXlS8E_02_04_01 | 2 | 1 | Paragraph | WNev_iSes_02_03_03 | 2 | An imitator steering a selfdriving car, for instance, would learn how to infer the weather condition from the dynamics ofthe car on the road. | An imitator steering a self-driving car, for instance, would learn how to infer the weather condition from the dynamics of the car on the road. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
23,
34
],
"sentence-2-token-indices": [
23,
35
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
119,
124
],
"sentence-2-token-indices": [
120,
126
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00030 | 4 | 1 | 3 | 3 |
31 | 0 | Paragraph | Y6LzLWXlS8E_02_04_02 | 2 | 1 | Paragraph | WNev_iSes_02_03_04 | 2 | This inference model can be applied both to its own online experienceas well as to expert trajectories, allowing it to imitate the behavior adequate for the weather. | This inference model can be applied both to its own online experience as well as to expert trajectories, allowing it to imitate the behavior adequate for the weather. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
59,
71
],
"sentence-2-token-indices": [
59,
72
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00031 | 4 | 2 | 3 | 4 |
32 | 1 | Figure | Y6LzLWXlS8E_02_05_00 | 2 | 1 | Figure | WNev_iSes_02_04_00 | 2 | [Figure] | [Figure] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00032 | 5 | 0 | 4 | 0 |
33 | 1 | Paragraph | Y6LzLWXlS8E_02_06_00 | 2 | 1 | Paragraph | WNev_iSes_02_05_00 | 2 | Finally, our deconfounded imitation learning algorithm isdemonstrated in a multi-armed bandit problem. | Finally, our deconfounded imitation learning algorithm is demonstrated in a multi-armed bandit problem. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
55,
69
],
"sentence-2-token-indices": [
55,
70
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00033 | 6 | 0 | 5 | 0 |
34 | 1 | Paragraph | Y6LzLWXlS8E_02_06_01 | 2 | 1 | Paragraph | WNev_iSes_02_05_01 | 2 | We showthat the agent quickly adapts to the unobserved propertiesof the environment and then behaves optimally (section 5). | We show that the agent quickly adapts to the unobserved properties of the environment and then behaves optimally (Section 5). | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
3,
11
],
"sentence-2-token-indices": [
3,
12
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
55,
67
],
"sentence-2-token-indices": [
56,
69
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
111,
119
],
"sentence-2-token-indices": [
113,
121
],
"intention": "Improve-grammar-Typo"
},
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00034 | 6 | 1 | 5 | 1 |
35 | 1 | Section | Y6LzLWXlS8E_03_00_00 | 3 | 1 | Section | WNev_iSes_03_00_00 | 3 | Imitation learning and latent confounders | Imitation learning and latent confounders | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00035 | null | null | null | null |
36 | 1 | Paragraph | Y6LzLWXlS8E_03_00_00 | 3 | 1 | Paragraph | WNev_iSes_03_00_00 | 3 | We begin by introducing the problem of confounded imitation learning. | We begin by introducing the problem of confounded imitation learning. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00036 | 0 | 0 | 0 | 0 |
37 | 1 | Paragraph | Y6LzLWXlS8E_03_00_01 | 3 | 1 | Paragraph | WNev_iSes_03_00_01 | 3 | Following Ortega et al. | Following Ortega et al. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00037 | 0 | 1 | 0 | 1 |
38 | 1 | Paragraph | Y6LzLWXlS8E_03_00_02 | 3 | 1 | Paragraph | WNev_iSes_03_00_02 | 3 | [2021], we discusshow behavioral cloning fails in the presence of latent confounders. | [2021], we discuss how behavioral cloning fails in the presence of latent confounders. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
11,
21
],
"sentence-2-token-indices": [
11,
22
],
"intention": "Language"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00038 | 0 | 2 | 0 | 2 |
39 | 1 | Paragraph | Y6LzLWXlS8E_03_00_03 | 3 | 1 | Paragraph | WNev_iSes_03_00_03 | 3 | We then define the interventional policy, whichsolves the problem of confounded imitation learning. | We then define the interventional policy, which solves the problem of confounded imitation learning. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
42,
53
],
"sentence-2-token-indices": [
42,
54
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00039 | 0 | 3 | 0 | 3 |
40 | 1 | Section | Y6LzLWXlS8E_04_00_00 | 4 | 1 | Section | WNev_iSes_04_00_00 | 4 | 2.1 Imitation learning | 2.1 Imitation learning | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00040 | null | null | null | null |
41 | 1 | Paragraph | Y6LzLWXlS8E_04_00_00 | 4 | 1 | Paragraph | WNev_iSes_04_00_00 | 4 | Imitation learning learns a policy from a dataset of expert demonstrations via supervised learning. | Imitation learning learns a policy from a dataset of expert demonstrations via supervised learning. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00041 | 0 | 0 | 0 | 0 |
42 | 1 | Paragraph | Y6LzLWXlS8E_04_01_00 | 4 | 1 | Paragraph | WNev_iSes_04_00_01 | 4 | The expert is a policy that acts in a (reward-free) | The expert is a policy that acts in a (reward-free) | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00042 | 1 | 0 | 0 | 1 |
43 | 1 | Paragraph | Y6LzLWXlS8E_04_01_01 | 4 | 1 | Paragraph | WNev_iSes_04_00_02 | 4 | Markov decision process (MDP) defined by a tuple M = ( S , A , P ( s ′ | s, a ) , P ( s 0 )) , where S is the set of states, A is the set of actions, P ( s ′ | s, a ) isthe transition probability, and P ( s 0 ) is a distribution over initial states. | Markov decision process (MDP) defined by a tuple M = ( S , A , P ( s ′ | s, a ) , P ( s 0 )) , where S is the set of states, A is the set of actions, P ( s ′ | s, a ) is the transition probability, and P ( s 0 ) is a distribution over initial states. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
167,
172
],
"sentence-2-token-indices": [
167,
173
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00043 | 1 | 1 | 0 | 2 |
44 | 1 | Paragraph | Y6LzLWXlS8E_04_02_01 | 4 | 1 | Paragraph | WNev_iSes_04_00_03 | 4 | The expert’s interaction withthe environment produces a trajectory τ = | The expert’s interaction with the environment produces a trajectory τ = | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
25,
32
],
"sentence-2-token-indices": [
25,
33
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00044 | 2 | 1 | 0 | 3 |
45 | 1 | Paragraph | Y6LzLWXlS8E_04_02_02 | 4 | 1 | Paragraph | WNev_iSes_04_00_04 | 4 | ( s 0 , a 0 , . . | ( s 0 , a 0 , . . | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00045 | 2 | 2 | 0 | 4 |
46 | 1 | Paragraph | Y6LzLWXlS8E_04_02_03 | 4 | 1 | Paragraph | WNev_iSes_04_00_05 | 4 | . , a T − 1 , s T ) . | . , a T − 1 , s T ) . | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00046 | 2 | 3 | 0 | 5 |
47 | 1 | Paragraph | Y6LzLWXlS8E_04_02_04 | 4 | 1 | Paragraph | WNev_iSes_04_00_06 | 4 | The expert may maximize theexpectation over some reward function, but this is not necessary (and some tasks cannot be expressedthrough Markov rewards Abel et al. | The expert may maximize the expectation over some reward function, but this is not necessary (and some tasks cannot be expressed through Markov rewards Abel et al. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
24,
38
],
"sentence-2-token-indices": [
24,
39
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
118,
134
],
"sentence-2-token-indices": [
119,
136
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00047 | 2 | 4 | 0 | 6 |
48 | 1 | Paragraph | Y6LzLWXlS8E_04_02_05 | 4 | 1 | Paragraph | WNev_iSes_04_01_00 | 4 | In the simplest form of imitation learning, a behavioralcloning policy π η ( a | s ) parametrized by η is learned by minimizing the loss − (cid:80) s,a ∈D log π η ( a | s ) ,where D is the dataset of state-action pairs collected by the expert’s policy. | In the simplest form of imitation learning, a behavioral cloning policy π η ( a | s ) parametrized by η is learned by minimizing the loss − (cid:80) s,a ∈D log π η ( a | s ) , where D is the dataset of state-action pairs collected by the expert’s policy. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
46,
63
],
"sentence-2-token-indices": [
46,
64
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
173,
179
],
"sentence-2-token-indices": [
174,
181
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00048 | 2 | 5 | 1 | 0 |
49 | 1 | Section | Y6LzLWXlS8E_05_00_00 | 5 | 1 | Section | WNev_iSes_05_00_00 | 5 | 2.2 Confounded imitation learning | 2.2 Confounded imitation learning | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00049 | null | null | null | null |
50 | 1 | Paragraph | Y6LzLWXlS8E_05_00_00 | 5 | 1 | Paragraph | WNev_iSes_05_00_00 | 5 | We now extend the imitation learning setup to allow for some variables θ ∈ Θ that are observed bythe expert, but not the imitator. | We now extend the imitation learning setup to allow for some variables θ ∈ Θ that are observed by the expert, but not the imitator. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
95,
100
],
"sentence-2-token-indices": [
95,
101
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00050 | 0 | 0 | 0 | 0 |
51 | 1 | Paragraph | Y6LzLWXlS8E_05_00_01 | 5 | 1 | Paragraph | WNev_iSes_05_00_01 | 5 | We define a family of Markov Decision processes as a latent space Θ ,a distribution P ( θ ) , and for each θ ∈ Θ , a reward-free MDP M θ = ( S , A , P ( s ′ | s, a, θ ) , P ( s 0 | θ )) . | We define a family of Markov Decision processes as a latent space Θ , a distribution P ( θ ) , and for each θ ∈ Θ , a reward-free MDP M θ = ( S , A , P ( s ′ | s, a, θ ) , P ( s 0 | θ )) . | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
68,
70
],
"sentence-2-token-indices": [
68,
71
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00051 | 0 | 1 | 0 | 1 |
52 | 1 | Paragraph | Y6LzLWXlS8E_05_01_00 | 5 | 1 | Paragraph | WNev_iSes_05_01_00 | 5 | We assume there exists an expert policy π exp ( a | s, θ ) for each MDP. | We assume there exists an expert policy π exp ( a | s, θ ) for each MDP. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00052 | 1 | 0 | 1 | 0 |
53 | 1 | Paragraph | Y6LzLWXlS8E_05_01_01 | 5 | 1 | Paragraph | WNev_iSes_05_01_01 | 5 | When it interacts with theenvironment, it generates the following distribution over trajectories τ : | When it interacts with the environment, it generates the following distribution over trajectories τ : | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
23,
38
],
"sentence-2-token-indices": [
23,
39
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00053 | 1 | 1 | 1 | 1 |
54 | 1 | Equation | Y6LzLWXlS8E_05_02_00 | 5 | 1 | Equation | WNev_iSes_05_02_00 | 5 | [Equation] | [Equation] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00054 | 2 | 0 | 2 | 0 |
55 | 1 | Paragraph | Y6LzLWXlS8E_05_03_00 | 5 | 1 | Paragraph | WNev_iSes_05_03_00 | 5 | The imitator does not observe the latent θ . | The imitator does not observe the latent θ . | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00055 | 3 | 0 | 3 | 0 |
56 | 1 | Paragraph | Y6LzLWXlS8E_05_03_01 | 5 | 1 | Paragraph | WNev_iSes_05_03_01 | 5 | It may thus need to implicitly infer it from the pasttransitions, so we take it to be a non-Markovian policy π η ( a t | s 1 , a 1 , . . . | It may thus need to implicitly infer it from the past transitions, so we take it to be a non-Markovian policy π η ( a t | s 1 , a 1 , . . . | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
49,
65
],
"sentence-2-token-indices": [
49,
66
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00056 | 3 | 1 | 3 | 1 |
57 | 1 | Paragraph | Y6LzLWXlS8E_05_03_02 | 5 | 1 | Paragraph | WNev_iSes_05_03_02 | 5 | , s t ) , parameterized by η . | , s t ) , parameterized by η . | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00057 | 3 | 2 | 3 | 2 |
58 | 1 | Paragraph | Y6LzLWXlS8E_05_04_00 | 5 | 1 | Paragraph | WNev_iSes_05_04_00 | 5 | The imitator generates the following distribution over trajectories: | The imitator generates the following distribution over trajectories: | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00058 | 4 | 0 | 4 | 0 |
59 | 1 | Equation | Y6LzLWXlS8E_05_05_00 | 5 | 1 | Equation | WNev_iSes_05_05_00 | 5 | [Equation] | [Equation] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00059 | 5 | 0 | 5 | 0 |
60 | 1 | Paragraph | Y6LzLWXlS8E_05_06_00 | 5 | 1 | Paragraph | WNev_iSes_05_06_00 | 5 | The Bayesian networks associated to these distributions are shown in figure 1. | The Bayesian networks associated to these distributions are shown in Figure 1. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
69,
75
],
"sentence-2-token-indices": [
69,
75
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00060 | 6 | 0 | 6 | 0 |
61 | 1 | Paragraph | Y6LzLWXlS8E_05_07_00 | 5 | 1 | Paragraph | WNev_iSes_05_07_00 | 5 | The goal of imitation learning in this setting is to learn imitator parameters η such that when theimitator is executed in the environment, the imitator agrees with the expert’s decisions, meaning wewish to maximise | The goal of imitation learning in this setting is to learn imitator parameters η such that when the imitator is executed in the environment, the imitator agrees with the expert’s decisions, meaning we wish to maximise | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
96,
107
],
"sentence-2-token-indices": [
96,
108
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
197,
203
],
"sentence-2-token-indices": [
198,
205
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00061 | 7 | 0 | 7 | 0 |
62 | 1 | Equation | Y6LzLWXlS8E_05_08_00 | 5 | 2 | Equation | WNev_iSes_05_08_00 | 5 | [Equation] | [Equation] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00062 | 8 | 0 | 8 | 0 |
63 | 1 | Paragraph | Y6LzLWXlS8E_05_09_00 | 5 | 2 | Paragraph | WNev_iSes_05_09_00 | 5 | If the expert solves some task (e. g. maximizes some reward function), this amounts to solving thesame task. | If the expert solves some task (e. g. maximizes some reward function), this amounts to solving the same task. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
95,
102
],
"sentence-2-token-indices": [
95,
103
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00063 | 9 | 0 | 9 | 0 |
64 | 2 | Section | Y6LzLWXlS8E_06_00_00 | 6 | 2 | Section | WNev_iSes_06_00_00 | 6 | 2.3 Naive behavioral cloning | 2.3 Naive behavioral cloning | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00064 | null | null | null | null |
65 | 2 | Paragraph | Y6LzLWXlS8E_06_00_00 | 6 | 2 | Paragraph | WNev_iSes_06_00_00 | 6 | If we have access to a data set of expert demonstrations, one can learn an imitator via behavioralcloning on the expert’s demonstrations. | If we have access to a data set of expert demonstrations, one can learn an imitator via behavioral cloning on the expert’s demonstrations. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
88,
105
],
"sentence-2-token-indices": [
88,
106
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00065 | 0 | 0 | 0 | 0 |
66 | 2 | Paragraph | Y6LzLWXlS8E_06_00_01 | 6 | 2 | Paragraph | WNev_iSes_06_00_01 | 6 | At optimality, this learns the conditional policy : | At optimality, this learns the conditional policy : | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00066 | 0 | 1 | 0 | 1 |
67 | 2 | Equation | Y6LzLWXlS8E_06_01_00 | 6 | 2 | Equation | WNev_iSes_06_01_00 | 6 | [Equation] | [Equation] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00067 | 1 | 0 | 1 | 0 |
68 | 2 | Paragraph | Y6LzLWXlS8E_06_02_00 | 6 | 2 | Paragraph | WNev_iSes_06_02_00 | 6 | Following Ortega et al. | Following Ortega et al. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00068 | 2 | 0 | 2 | 0 |
69 | 2 | Paragraph | Y6LzLWXlS8E_06_02_01 | 6 | 2 | Paragraph | WNev_iSes_06_02_01 | 6 | [2021], consider the following example of a confounded multi-armed banditwith | [2021], consider the following example of a confounded multi-armed bandit with A = | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
67,
77
],
"sentence-2-token-indices": [
67,
82
],
"intention": "Content"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00069 | 2 | 1 | 2 | 1 |
70 | 2 | Paragraph | Y6LzLWXlS8E_06_02_02 | 6 | 2 | Paragraph | WNev_iSes_06_02_02 | 6 | A = Θ = { 1 , . . | Θ = { 1 , . . | {
"0": {
"type": "Deletion",
"sentence-1-token-indices": [
0,
3
],
"sentence-2-token-indices": null,
"intention": "Language"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00070 | 2 | 2 | 2 | 2 |
71 | 2 | Paragraph | Y6LzLWXlS8E_06_02_03 | 6 | 2 | Paragraph | WNev_iSes_06_02_03 | 6 | , 5 } and S = { 0 , 1 } : | , 5 } and S = { 0 , 1 } : | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00071 | 2 | 3 | 2 | 3 |
72 | 2 | Equation | Y6LzLWXlS8E_06_03_00 | 6 | 2 | Equation | WNev_iSes_06_03_00 | 6 | [Equation] | [Equation] [Equation] | {
"0": {
"type": "Insertion",
"sentence-1-token-indices": null,
"sentence-2-token-indices": [
11,
21
],
"intention": "Format"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00072 | 3 | 0 | 3 | 0 |
73 | 2 | Paragraph | Y6LzLWXlS8E_06_04_00 | 6 | 2 | Paragraph | WNev_iSes_06_05_00 | 6 | The expert knows which bandit arm is special (and labeled by θ ) and pulls it with high probability,while the imitating agent does not have access to this information. | The expert knows which bandit arm is special (and labeled by θ ) and pulls it with high probability, while the imitating agent does not have access to this information. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
88,
105
],
"sentence-2-token-indices": [
88,
106
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00073 | 4 | 0 | 5 | 0 |
74 | 2 | Paragraph | Y6LzLWXlS8E_06_05_00 | 6 | 2 | Paragraph | WNev_iSes_06_06_00 | 6 | If we roll out the naive behavioral cloning policy in this environment, shown in Figure 2, we see thecausal delusion at work. | If we roll out the naive behavioral cloning policy in this environment, shown in Figure 2, we see the causal delusion at work. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
98,
107
],
"sentence-2-token-indices": [
98,
108
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00074 | 5 | 0 | 6 | 0 |
75 | 2 | Paragraph | Y6LzLWXlS8E_06_05_01 | 6 | 2 | Paragraph | WNev_iSes_06_06_01 | 6 | At time t , the latent that is inferred by p cond takes past actions as evidencefor the latent variable. | At time t , the latent that is inferred by p cond takes past actions as evidence for the latent variable. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
72,
83
],
"sentence-2-token-indices": [
72,
84
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00075 | 5 | 1 | 6 | 1 |
76 | 2 | Paragraph | Y6LzLWXlS8E_06_05_02 | 6 | 2 | Paragraph | WNev_iSes_06_06_02 | 6 | This makes sense on the expert demonstrations, as the expert is cognizantof the latent variable. | This makes sense on the expert demonstrations, as the expert is cognizant of the latent variable. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
64,
75
],
"sentence-2-token-indices": [
64,
76
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00076 | 5 | 2 | 6 | 2 |
77 | 2 | Paragraph | Y6LzLWXlS8E_06_05_03 | 6 | 2 | Paragraph | WNev_iSes_06_06_03 | 6 | However, during an imitator roll-out, the past actions are not evidence of thelatent, as the imitator is blind to it. | However, during an imitator roll-out, the past actions are not evidence of the latent, as the imitator is blind to it. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
75,
85
],
"sentence-2-token-indices": [
75,
86
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00077 | 5 | 3 | 6 | 3 |
78 | 2 | Paragraph | Y6LzLWXlS8E_06_05_04 | 6 | 2 | Paragraph | WNev_iSes_06_06_04 | 6 | Concretely, the imitator will take its first action uniformly andlater tends to repeat that action, as it mistakenly takes the first action to be evidence for the latent. | Concretely, the imitator will take its first action uniformly and later tends to repeat that action, as it mistakenly takes the first action to be evidence for the latent. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
62,
70
],
"sentence-2-token-indices": [
62,
71
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00078 | 5 | 4 | 6 | 4 |
79 | 2 | Section | Y6LzLWXlS8E_07_00_00 | 7 | 2 | Section | WNev_iSes_07_00_00 | 7 | 2.4 Interventional policy | 2.4 Interventional policy | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00079 | null | null | null | null |
80 | 2 | Paragraph | Y6LzLWXlS8E_07_00_00 | 7 | 2 | Paragraph | WNev_iSes_07_00_00 | 7 | A solution to this issue is to only take as evidence the data that was actually informed by the latent,which are the transitions. | A solution to this issue is to only take as evidence the data that was actually informed by the latent, which are the transitions. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
96,
108
],
"sentence-2-token-indices": [
96,
109
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00080 | 0 | 0 | 0 | 0 |
81 | 2 | Paragraph | Y6LzLWXlS8E_07_00_01 | 7 | 2 | Paragraph | WNev_iSes_07_00_01 | 7 | This defines the following imitator policy: | This defines the following imitator policy: | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00081 | 0 | 1 | 0 | 1 |
82 | 2 | Equation | Y6LzLWXlS8E_07_01_00 | 7 | 2 | Equation | WNev_iSes_07_01_00 | 7 | [Equation] | [Equation] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00082 | 1 | 0 | 1 | 0 |
83 | 2 | Paragraph | Y6LzLWXlS8E_07_02_00 | 7 | 2 | Paragraph | WNev_iSes_07_02_00 | 7 | In a causal framework, that corresponds to treating the choice of past actions as interventions. | In a causal framework, that corresponds to treating the choice of past actions as interventions. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00083 | 2 | 0 | 2 | 0 |
84 | 2 | Paragraph | Y6LzLWXlS8E_07_02_01 | 7 | 2 | Paragraph | WNev_iSes_07_02_01 | 7 | Inthe notation of the do-calculus [Pearl, 2009], this equals p ( a t | s 1 , do( a 1 ) , s 2 , do( a 2 ) , . . . | In the notation of the do-calculus [Pearl, 2009], this equals p ( a t | s 1 , do( a 1 ) , s 2 , do( a 2 ) , . . . | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
0,
5
],
"sentence-2-token-indices": [
0,
6
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00084 | 2 | 1 | 2 | 1 |
85 | 2 | Paragraph | Y6LzLWXlS8E_07_02_02 | 7 | 2 | Paragraph | WNev_iSes_07_02_02 | 7 | , s t ) . | , s t ) . | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00085 | 2 | 2 | 2 | 2 |
86 | 2 | Paragraph | Y6LzLWXlS8E_07_02_03 | 7 | 2 | Paragraph | WNev_iSes_07_02_03 | 7 | Thepolicy in equation (5) is therefore known as interventional policy [Ortega et al., 2021]. | The policy in Equation (5) is therefore known as interventional policy [Ortega et al., 2021]. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
0,
9
],
"sentence-2-token-indices": [
0,
10
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
13,
21
],
"sentence-2-token-indices": [
14,
22
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00086 | 2 | 3 | 2 | 3 |
87 | 2 | Section | Y6LzLWXlS8E_08_00_00 | 8 | null | null | null | null | Deconfounding imitation learning | {
"0": {
"type": "Deletion",
"sentence-1-token-indices": [
0,
32
],
"sentence-2-token-indices": null,
"intention": "Content"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00087 | null | null | null | null |
|
88 | 2 | Figure | Y6LzLWXlS8E_08_00_00 | 8 | 2 | Figure | WNev_iSes_07_03_00 | 7 | [Figure] | [Figure] | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00088 | 0 | 0 | 3 | 0 |
90 | 2 | Paragraph | Y6LzLWXlS8E_08_01_00 | 8 | 3 | Paragraph | WNev_iSes_08_00_00 | 8 | We now present our theoretical results on how imitation learning can be deconfounded. | We now present our theoretical results on how imitation learning can be deconfounded. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00090 | 1 | 0 | 0 | 0 |
91 | 2 | Paragraph | Y6LzLWXlS8E_08_01_01 | 8 | 3 | Paragraph | WNev_iSes_08_00_01 | 8 | We first showthat the interventional policy is optimal in some sense, before analyzing in which settings it can belearned. | We first show that the interventional policy is optimal in some sense, before analyzing in which settings it can be learned. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
9,
17
],
"sentence-2-token-indices": [
9,
18
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
112,
122
],
"sentence-2-token-indices": [
113,
124
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00091 | 1 | 1 | 0 | 1 |
92 | 2 | Section | Y6LzLWXlS8E_09_00_00 | 9 | 3 | Section | WNev_iSes_09_00_00 | 9 | 3.1 Optimality of the interventional policy | 3.1 Optimality of the interventional policy | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00092 | null | null | null | null |
93 | 2 | Figure | Y6LzLWXlS8E_09_00_00 | 9 | 3 | Paragraph | WNev_iSes_09_00_00 | 9 | [Figure] Under some reasonable assumptions, the interventional policy approaches the expert’s policy, as weprove in the appendix 2. [Figure] | Under some reasonable assumptions, the interventional policy approaches the expert’s policy, as we prove in the Appendix B. | {
"0": {
"type": "Deletion",
"sentence-1-token-indices": [
0,
8
],
"sentence-2-token-indices": null,
"intention": "Format"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
105,
112
],
"sentence-2-token-indices": [
96,
104
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
120,
140
],
"sentence-2-token-indices": [
112,
123
],
"intention": "Format"
},
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00093 | 0 | 0 | 0 | 0 |
94 | 3 | Paragraph | Y6LzLWXlS8E_09_03_00 | 9 | 3 | Paragraph | WNev_iSes_09_01_00 | 9 | Theorem 3.1 (Informal) . | Theorem 3.1 (Informal) . | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00094 | 3 | 0 | 1 | 0 |
95 | 3 | Paragraph | Y6LzLWXlS8E_09_03_01 | 9 | 3 | Paragraph | WNev_iSes_09_01_01 | 9 | If the interventional inference p int ( θ | τ <t ) approaches the true latent of theenvironment as t → ∞ on the rollouts of π int , and if the expert maximises some reward that is fixedacross all environments, then as t → ∞ , the imitator policy π int ( a t | s ) approaches the expert policy. | If the interventional inference p int ( θ | τ <t ) approaches the true latent of the environment as t → ∞ on the rollouts of π int , and if the expert maximises some reward that is fixed across all environments, then as t → ∞ , the imitator policy π int ( a t | s ) approaches the expert policy. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
81,
95
],
"sentence-2-token-indices": [
81,
96
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
180,
191
],
"sentence-2-token-indices": [
181,
193
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00095 | 3 | 1 | 1 | 1 |
96 | 3 | Paragraph | Y6LzLWXlS8E_09_04_00 | 9 | 3 | Paragraph | WNev_iSes_09_02_00 | 9 | Proof. | Proof. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00096 | 4 | 0 | 2 | 0 |
97 | 3 | Paragraph | Y6LzLWXlS8E_09_04_01 | 9 | 3 | Paragraph | WNev_iSes_09_02_01 | 9 | See lemma 2.1 in the appendix. | See Lemma 2.1 in the appendix. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
4,
9
],
"sentence-2-token-indices": [
4,
9
],
"intention": "Improve-grammar-Typo"
},
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00097 | 4 | 1 | 2 | 1 |
98 | 3 | Paragraph | Y6LzLWXlS8E_09_05_00 | 9 | 3 | Paragraph | WNev_iSes_09_03_00 | 9 | The requirement here means that the transition dynamics must be informative about the latent —we consider latent confounders that manifest in the dynamics, not those that affect only the agentbehavior. | The requirement here means that the transition dynamics must be informative about the latent — we consider latent confounders that manifest in the dynamics, not those that affect only the agent behavior. | {
"0": {
"type": "Substitute",
"sentence-1-token-indices": [
93,
96
],
"sentence-2-token-indices": [
93,
97
],
"intention": "Improve-grammar-Typo"
},
"1": {
"type": "Substitute",
"sentence-1-token-indices": [
187,
201
],
"sentence-2-token-indices": [
188,
203
],
"intention": "Improve-grammar-Typo"
},
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00098 | 5 | 0 | 3 | 0 |
99 | 3 | Paragraph | Y6LzLWXlS8E_09_05_01 | 9 | 3 | Paragraph | WNev_iSes_09_03_01 | 9 | In this case, the interventional policy thus presents a solution to the confounding problem. | In this case, the interventional policy thus presents a solution to the confounding problem. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00099 | 5 | 1 | 3 | 1 |
100 | 3 | Paragraph | Y6LzLWXlS8E_09_06_00 | 9 | 3 | Paragraph | WNev_iSes_09_04_00 | 9 | In the rest of this paper we focus on the question if and how it can be learned from data. | In the rest of this paper we focus on the question if and how it can be learned from data. | {
"0": null,
"1": null,
"2": null,
"3": null,
"4": null,
"5": null,
"6": null,
"7": null,
"8": null,
"9": null,
"10": null
} | Y6LzLWXlS8E | WNev_iSes | Y6LzLWXlS8E.WNev_iSes.00100 | 6 | 0 | 4 | 0 |
End of preview.
Repository in construction.
- Downloads last month
- 81