Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System Article Swipe
Ruining Zhang
,
Haoran Han
,
Maolong Lv
,
Qisong Yang
,
Jian Cheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i15.29623
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i15.29623
Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the underlying factors contributing to such performance deterioration when dealing with expanded state spaces, using a novel analysis technique known as state division. In contrast to prior approaches that employ state division merely as a post-hoc explanatory tool, our methodology delves into the intrinsic characteristics of DRL policy networks. Specifically, we demonstrate that the expansion of state space induces the activation function $\tanh$ to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear. Our analysis centers on the paradigm of the double-integrator system, revealing that this gradual shift towards linearity imparts a control behavior reminiscent of bang-bang control. However, the inherent linearity of the division boundary prevents the attainment of an ideal bang-bang control, thereby introducing unavoidable overshooting. Our experimental investigations, employing diverse RL algorithms, establish that this performance phenomenon stems from inherent attributes of the DRL policy network, remaining consistent across various optimization algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v38i15.29623
- https://ojs.aaai.org/index.php/AAAI/article/download/29623/31057
- OA Status
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- Cited By
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- References
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https://doi.org/10.1609/aaai.v38i15.29623Digital Object Identifier
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Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator SystemWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-03-24Full publication date if available
- Authors
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Ruining Zhang, Haoran Han, Maolong Lv, Qisong Yang, Jian ChengList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v38i15.29623Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/29623/31057Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/29623/31057Direct OA link when available
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Generalization, Integrator, Computer science, Mathematics, Telecommunications, Mathematical analysis, Bandwidth (computing)Top concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.policy | 7, 89, 185 |
| abstract_inverted_index.raised | 15 |
| abstract_inverted_index.spaces | 23 |
| abstract_inverted_index.$\tanh$ | 104 |
| abstract_inverted_index.centers | 123 |
| abstract_inverted_index.control | 12, 140 |
| abstract_inverted_index.dealing | 51 |
| abstract_inverted_index.diverse | 10, 170 |
| abstract_inverted_index.exhibit | 106 |
| abstract_inverted_index.factors | 44 |
| abstract_inverted_index.gradual | 134 |
| abstract_inverted_index.imparts | 138 |
| abstract_inverted_index.induces | 100 |
| abstract_inverted_index.linear. | 120 |
| abstract_inverted_index.spaces, | 55 |
| abstract_inverted_index.system, | 130 |
| abstract_inverted_index.thereby | 162 |
| abstract_inverted_index.towards | 136 |
| abstract_inverted_index.uncover | 41 |
| abstract_inverted_index.various | 190 |
| abstract_inverted_index.However, | 146 |
| abstract_inverted_index.analysis | 59, 122 |
| abstract_inverted_index.behavior | 141 |
| abstract_inverted_index.boundary | 116, 153 |
| abstract_inverted_index.contrast | 66 |
| abstract_inverted_index.control, | 161 |
| abstract_inverted_index.control. | 145 |
| abstract_inverted_index.division | 73, 115, 152 |
| abstract_inverted_index.expanded | 53 |
| abstract_inverted_index.function | 103 |
| abstract_inverted_index.inherent | 148, 180 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.network, | 186 |
| abstract_inverted_index.networks | 8 |
| abstract_inverted_index.paradigm | 126 |
| abstract_inverted_index.post-hoc | 77 |
| abstract_inverted_index.prevents | 154 |
| abstract_inverted_index.training | 35 |
| abstract_inverted_index.Extensive | 0 |
| abstract_inverted_index.bang-bang | 144, 160 |
| abstract_inverted_index.division. | 64 |
| abstract_inverted_index.employing | 169 |
| abstract_inverted_index.establish | 173 |
| abstract_inverted_index.expansion | 96 |
| abstract_inverted_index.expansive | 21 |
| abstract_inverted_index.intrinsic | 85 |
| abstract_inverted_index.linearity | 137, 149 |
| abstract_inverted_index.networks. | 90 |
| abstract_inverted_index.nonlinear | 118 |
| abstract_inverted_index.questions | 16 |
| abstract_inverted_index.regarding | 17 |
| abstract_inverted_index.remaining | 187 |
| abstract_inverted_index.resulting | 108 |
| abstract_inverted_index.revealing | 131 |
| abstract_inverted_index.technique | 60 |
| abstract_inverted_index.activation | 102 |
| abstract_inverted_index.approaches | 69 |
| abstract_inverted_index.attainment | 156 |
| abstract_inverted_index.attributes | 181 |
| abstract_inverted_index.consistent | 188 |
| abstract_inverted_index.continuous | 11 |
| abstract_inverted_index.phenomenon | 177 |
| abstract_inverted_index.underlying | 43 |
| abstract_inverted_index.algorithms, | 172 |
| abstract_inverted_index.algorithms. | 192 |
| abstract_inverted_index.degradation | 19 |
| abstract_inverted_index.demonstrate | 93 |
| abstract_inverted_index.explanatory | 78 |
| abstract_inverted_index.introducing | 163 |
| abstract_inverted_index.methodology | 81 |
| abstract_inverted_index.performance | 18, 48, 176 |
| abstract_inverted_index.reminiscent | 142 |
| abstract_inverted_index.unavoidable | 164 |
| abstract_inverted_index.utilization | 1 |
| abstract_inverted_index.contributing | 45 |
| abstract_inverted_index.environment. | 36 |
| abstract_inverted_index.experimental | 167 |
| abstract_inverted_index.optimization | 191 |
| abstract_inverted_index.Specifically, | 91 |
| abstract_inverted_index.deterioration | 49 |
| abstract_inverted_index.overshooting. | 165 |
| abstract_inverted_index.reinforcement | 4 |
| abstract_inverted_index.saturability, | 107 |
| abstract_inverted_index.transformation | 111 |
| abstract_inverted_index.characteristics | 86 |
| abstract_inverted_index.investigations, | 168 |
| abstract_inverted_index.double-integrator | 129 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.60763889 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |