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?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.10472
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.10472
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.
Related Topics
Concepts
Computer science
State space
Reinforcement learning
Integrator
Generalization
Nonlinear system
State (computer science)
Division (mathematics)
Control theory (sociology)
Control (management)
Mathematics
Artificial intelligence
Algorithm
Telecommunications
Arithmetic
Statistics
Quantum mechanics
Mathematical analysis
Physics
Bandwidth (computing)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.10472
- https://arxiv.org/pdf/2312.10472
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389983101
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389983101Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.10472Digital Object Identifier
- Title
-
Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator SystemWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-16Full publication date if available
- Authors
-
Ruining Zhang, Haoran Han, Maolong Lv, Qisong Yang, Jian ChengList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.10472Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.10472Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.10472Direct OA link when available
- Concepts
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Computer science, State space, Reinforcement learning, Integrator, Generalization, Nonlinear system, State (computer science), Division (mathematics), Control theory (sociology), Control (management), Mathematics, Artificial intelligence, Algorithm, Telecommunications, Arithmetic, Statistics, Quantum mechanics, Mathematical analysis, Physics, Bandwidth (computing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| 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 | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |