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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('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '1': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '2': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '3': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '4': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '5': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '6': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '7': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '8': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '9': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '10': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/table.py", line 2092, 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('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '1': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '2': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '3': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '4': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '5': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '6': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '7': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '8': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '9': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}, '10': {'type': Value('string'), 'sentence-1-token-indices': List(Value('int64')), 'sentence-2-token-indices': List(Value('int64')), 'intention': Value('string')}}
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 1450, 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 993, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1858, 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
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|
Title
|
WNev_iSes_00_00_00
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|
Deconfounded Imitation Learning
|
Deconfounded Imitation Learning
|
{
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WNev_iSes
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1
| 0
|
Paragraph
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Y6LzLWXlS8E_01_00_00
| 1
| null | null | null | null |
Anonymous Author(s)
|
{
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"intention": "Content"
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Y6LzLWXlS8E
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WNev_iSes
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2
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AffiliationAddress email
|
{
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|
Y6LzLWXlS8E
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WNev_iSes
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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,
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|
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,
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"6": null,
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}
|
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,
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],
"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
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"sentence-2-token-indices": [
85,
91
],
"intention": "Content"
},
"3": null,
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}
|
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.
|
{
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"sentence-1-token-indices": [
0,
7
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8
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"intention": "Improve-grammar-Typo"
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"1": {
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"sentence-1-token-indices": [
78,
91
],
"sentence-2-token-indices": [
79,
93
],
"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
163,
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],
"sentence-2-token-indices": [
165,
171
],
"intention": "Improve-grammar-Typo"
},
"3": null,
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}
|
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,
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"6": null,
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}
|
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,
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}
|
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
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"intention": "Improve-grammar-Typo"
},
"2": {
"type": "Substitute",
"sentence-1-token-indices": [
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},
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}
|
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
|
{
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|
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,
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],
"intention": "Improve-grammar-Typo"
},
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}
|
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": {
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"sentence-1-token-indices": [
142,
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"sentence-2-token-indices": [
142,
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"intention": "Improve-grammar-Typo"
},
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}
|
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": {
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"sentence-1-token-indices": [
56,
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],
"sentence-2-token-indices": [
56,
65
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"intention": "Improve-grammar-Typo"
},
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In particular, they assume thatthere are no latent confounders —variables that affect the expert behavior, but that are not observed bythe agent.
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In particular, they assume that there are no latent confounders —variables that affect the expert behavior, but that are not observed by the agent.
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This assumption is often unrealistic.
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This assumption is often unrealistic.
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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.
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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.
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Animitator agent without access to the weather forecast will not be able to adapt to such conditions.
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An imitator agent without access to the weather forecast will not be able to adapt to such conditions.
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In such a situation, an imitating agent may take their own past actions as evidence for the values ofthe confounder.
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In such a situation, an imitating agent may take their own past actions as evidence for the values of the confounder.
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A self-driving car, for instance, could conclude that it is driving fast, thus there can beno ice on the road.
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A self-driving car, for instance, could conclude that it is driving fast, thus there can be no ice on the road.
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This issue of causal delusion was first pointed out in Ortega and Braun [2010a,b]and studied in more depth by Ortega et al.
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This issue of causal delusion was first pointed out in Ortega and Braun [2010a,b] and studied in more depth by Ortega et al.
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The authors analyze the causal structure of thisproblem and argue that an imitator needs to learn a policy that corresponds to a certain interventionaldistribution.
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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.
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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.
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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.
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In this paper, we present a solution to a confounded IL problem, where both the expert policy andthe environment dynamics are Markovian.
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In this paper, we present a solution to a confounded IL problem, where both the expert policy and the environment dynamics are Markovian.
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The solution does not require querying experts.
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The solution does not require querying experts.
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We firstpresent a characterization of confounded IL problems depending on properties of the environmentand expert policy (section 3).
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We first present a characterization of confounded IL problems depending on properties of the environment and expert policy (Section 3).
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We then show theoretically that an imitating agent can learn behaviorsthat approach optimality when the above Markov assumptions and a recurrence property hold.
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We then show theoretically that an imitating agent can learn behaviors that approach optimality when the above Markov assumptions and a recurrence property hold.
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We then introduce a practical algorithm for deconfounded imitation learning that does not require expert queries (section 4).
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We then introduce a practical algorithm for deconfounded imitation learning that does not require expert queries (Section 4).
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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.
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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.
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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.
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WNev_iSes_02_03_04
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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.
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Y6LzLWXlS8E.WNev_iSes.00031
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Y6LzLWXlS8E_02_05_00
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Figure
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WNev_iSes_02_04_00
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[Figure]
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[Figure]
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WNev_iSes_02_05_00
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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.
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We showthat the agent quickly adapts to the unobserved propertiesof the environment and then behaves optimally (section 5).
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We show that the agent quickly adapts to the unobserved properties of the environment and then behaves optimally (Section 5).
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Section
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Y6LzLWXlS8E_03_00_00
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Section
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WNev_iSes_03_00_00
| 3
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Imitation learning and latent confounders
|
Imitation learning and latent confounders
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WNev_iSes_03_00_00
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We begin by introducing the problem of confounded imitation learning.
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We begin by introducing the problem of confounded imitation learning.
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Following Ortega et al.
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Following Ortega et al.
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WNev_iSes_03_00_02
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[2021], we discusshow behavioral cloning fails in the presence of latent confounders.
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[2021], we discuss how behavioral cloning fails in the presence of latent confounders.
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We then define the interventional policy, whichsolves the problem of confounded imitation learning.
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We then define the interventional policy, which solves the problem of confounded imitation learning.
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Section
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Y6LzLWXlS8E_04_00_00
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Section
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WNev_iSes_04_00_00
| 4
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2.1 Imitation learning
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2.1 Imitation learning
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WNev_iSes_04_00_00
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Imitation learning learns a policy from a dataset of expert demonstrations via supervised learning.
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Imitation learning learns a policy from a dataset of expert demonstrations via supervised learning.
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WNev_iSes_04_00_01
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The expert is a policy that acts in a (reward-free)
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The expert is a policy that acts in a (reward-free)
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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.
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WNev_iSes_04_00_03
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The expert’s interaction withthe environment produces a trajectory τ =
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The expert’s interaction with the environment produces a trajectory τ =
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( s 0 , a 0 , . .
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( s 0 , a 0 , . .
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WNev_iSes_04_00_05
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. , a T − 1 , s T ) .
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. , a T − 1 , s T ) .
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WNev_iSes_04_00_06
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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.
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WNev_iSes_04_01_00
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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.
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Section
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Y6LzLWXlS8E_05_00_00
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Section
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WNev_iSes_05_00_00
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2.2 Confounded imitation learning
|
2.2 Confounded imitation learning
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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.
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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 )) .
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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.
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When it interacts with theenvironment, it generates the following distribution over trajectories τ :
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When it interacts with the environment, it generates the following distribution over trajectories τ :
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Y6LzLWXlS8E.WNev_iSes.00053
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Equation
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Equation
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WNev_iSes_05_02_00
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[Equation]
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[Equation]
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The imitator does not observe the latent θ .
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The imitator does not observe the latent θ .
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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 , . . .
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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 , . . .
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, s t ) , parameterized by η .
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, s t ) , parameterized by η .
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The imitator generates the following distribution over trajectories:
|
The imitator generates the following distribution over trajectories:
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[Equation]
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[Equation]
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The Bayesian networks associated to these distributions are shown in figure 1.
|
The Bayesian networks associated to these distributions are shown in Figure 1.
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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
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"8": null,
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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]
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00062
| 8
| 0
| 8
| 0
|
63
| 1
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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.
|
{
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00063
| 9
| 0
| 9
| 0
|
64
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Section
|
Y6LzLWXlS8E_06_00_00
| 6
| 2
|
Section
|
WNev_iSes_06_00_00
| 6
|
2.3 Naive behavioral cloning
|
2.3 Naive behavioral cloning
|
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Y6LzLWXlS8E
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WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00064
| null | null | null | null |
65
| 2
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Paragraph
|
Y6LzLWXlS8E_06_00_00
| 6
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|
Paragraph
|
WNev_iSes_06_00_00
| 6
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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.
|
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00065
| 0
| 0
| 0
| 0
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66
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Paragraph
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Y6LzLWXlS8E_06_00_01
| 6
| 2
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Paragraph
|
WNev_iSes_06_00_01
| 6
|
At optimality, this learns the conditional policy :
|
At optimality, this learns the conditional policy :
|
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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]
|
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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.
|
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|
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 =
|
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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 , . .
|
{
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0,
3
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"8": null,
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"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 } :
|
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}
|
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]
|
{
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11,
21
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"intention": "Format"
},
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}
|
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.
|
{
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"sentence-2-token-indices": [
88,
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],
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|
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.
|
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|
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.
|
{
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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.
|
{
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64,
76
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|
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.
|
{
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75,
86
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|
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.
|
{
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"sentence-1-token-indices": [
62,
70
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"sentence-2-token-indices": [
62,
71
],
"intention": "Improve-grammar-Typo"
},
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|
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
|
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00079
| null | null | null | null |
80
| 2
|
Paragraph
|
Y6LzLWXlS8E_07_00_00
| 7
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|
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.
|
{
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Y6LzLWXlS8E
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WNev_iSes
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Y6LzLWXlS8E.WNev_iSes.00080
| 0
| 0
| 0
| 0
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81
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Paragraph
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Y6LzLWXlS8E_07_00_01
| 7
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Paragraph
|
WNev_iSes_07_00_01
| 7
|
This defines the following imitator policy:
|
This defines the following imitator policy:
|
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Y6LzLWXlS8E
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WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00081
| 0
| 1
| 0
| 1
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82
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|
Equation
|
Y6LzLWXlS8E_07_01_00
| 7
| 2
|
Equation
|
WNev_iSes_07_01_00
| 7
|
[Equation]
|
[Equation]
|
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Y6LzLWXlS8E
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WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00082
| 1
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| 1
| 0
|
83
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Paragraph
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Y6LzLWXlS8E_07_02_00
| 7
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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.
|
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00083
| 2
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| 2
| 0
|
84
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Paragraph
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Y6LzLWXlS8E_07_02_01
| 7
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|
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 ) , . . .
|
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00084
| 2
| 1
| 2
| 1
|
85
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|
Paragraph
|
Y6LzLWXlS8E_07_02_02
| 7
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|
Paragraph
|
WNev_iSes_07_02_02
| 7
|
, s t ) .
|
, s t ) .
|
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00085
| 2
| 2
| 2
| 2
|
86
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|
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].
|
{
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Y6LzLWXlS8E
|
WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00086
| 2
| 3
| 2
| 3
|
87
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|
Section
|
Y6LzLWXlS8E_08_00_00
| 8
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Deconfounding imitation learning
|
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Y6LzLWXlS8E
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WNev_iSes
|
Y6LzLWXlS8E.WNev_iSes.00087
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|
88
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|
Figure
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Y6LzLWXlS8E_08_00_00
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Figure
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[Figure]
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[Figure]
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We now present our theoretical results on how imitation learning can be deconfounded.
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We now present our theoretical results on how imitation learning can be deconfounded.
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We first showthat the interventional policy is optimal in some sense, before analyzing in which settings it can belearned.
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We first show that the interventional policy is optimal in some sense, before analyzing in which settings it can be learned.
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Section
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Section
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3.1 Optimality of the interventional policy
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3.1 Optimality of the interventional policy
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[Figure] Under some reasonable assumptions, the interventional policy approaches the expert’s policy, as weprove in the appendix 2. [Figure]
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Under some reasonable assumptions, the interventional policy approaches the expert’s policy, as we prove in the Appendix B.
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Theorem 3.1 (Informal) .
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Theorem 3.1 (Informal) .
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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.
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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.
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Proof.
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Proof.
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See lemma 2.1 in the appendix.
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See Lemma 2.1 in the appendix.
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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.
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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.
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In this case, the interventional policy thus presents a solution to the confounding problem.
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In this case, the interventional policy thus presents a solution to the confounding problem.
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In the rest of this paper we focus on the question if and how it can be learned from data.
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In the rest of this paper we focus on the question if and how it can be learned from data.
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End of preview.
CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions
About
This repository contains the CASIMIR dataset, a dataset of the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. Pairs of consecutive versions of an article are aligned at sentence-level while keeping paragraph location information as metadata for supporting future revision studies at the discourse level. Each pair of revised sentences is enriched with automatically extracted edits and associated revision intention.
The creation process of this dataset is described in the following references:
- CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions (Jourdan et al., LREC-COLING 2024)
- CASIMIR : un Corpus d’Articles Scientifiques Intégrant les ModIfications et Révisions des auteurs (Jourdan et al., JEP/TALN/RECITAL 2023)
Content
The dataset is composed of different subsets:
- article_pairs: Main datas, pairs of articles aligned at sentence level with their edits extracted. Divided in train, validation, test.
- mapping: Mapping of the OpenReview forum id with the associated articles versions id. Divided in train, validation, test.
- mapping_full_corpus: Same data as previous point but for the whole corpus at once.
- article_pairs_small_test and mapping_small_test: As running inference on large models over the full test set is computationally expensive and time-consuming, we also provide a smaller test subset, representing 30% of the original test set (approximately 3% of the full dataset, i.e. 468 papers).
- reviews: All comments posted on the article's forum on OpenReview.
- metadata: dates, authors, keywords, venue, ids, ...
The dataset article_pairs subset is divided into the following three splits:
| Split | # articles | # versions/article (avg) | # edits/pair (avg) | Edit length (avg) | % (Label) Content | % (Label) Grammar-Typo | % (Label) Format | % (Label) Language |
|---|---|---|---|---|---|---|---|---|
| Train | 12 488 | 3.49 | 141.85 | 34.90 | 41.99 | 22.69 | 20.44 | 14.88 |
| Test | 1561 | 3.51 | 142.98 | 35.24 | 42.70 | 21.43 | 20.47 | 15.40 |
| Validation | 1597 | 3.49 | 143.38 | 34.35 | 41.13 | 24.35 | 19.79 | 14.73 |
The following data fields are available:
- WIP
Please cite this work as:
@inproceedings{jourdan-etal-2024-casimir,
title = "{CASIMIR}: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions",
author = "Jourdan, L{\'e}ane and
Boudin, Florian and
Hernandez, Nicolas and
Dufour, Richard",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.257/",
pages = "2883--2892",
abstract = "Writing a scientific article is a challenging task as it is a highly codified and specific genre, consequently proficiency in written communication is essential for effectively conveying research findings and ideas. In this article, we propose an original textual resource on the revision step of the writing process of scientific articles. This new dataset, called CASIMIR, contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. Pairs of consecutive versions of an article are aligned at sentence-level while keeping paragraph location information as metadata for supporting future revision studies at the discourse level. Each pair of revised sentences is enriched with automatically extracted edits and associated revision intention. To assess the initial quality on the dataset, we conducted a qualitative study of several state-of-the-art text revision approaches and compared various evaluation metrics. Our experiments led us to question the relevance of the current evaluation methods for the text revision task."
}
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