Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning Article Swipe
Ickjai Lee
,
Hoang-Giang Cao
,
Cong-Tinh Dao
,
Yu‐Cheng Chen
,
I‐Chen Wu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.04315
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.04315
Deep Reinforcement Learning (DRL) has achieved remarkable success, ranging from complex computer games to real-world applications, showing the potential for intelligent agents capable of learning in dynamic environments. However, its application in real-world scenarios presents challenges, including the jerky problem, in which jerky trajectories not only compromise system safety but also increase power consumption and shorten the service life of robotic and autonomous systems. To address jerky actions, a method called conditioning for action policy smoothness (CAPS) was proposed by adding regularization terms to reduce the action changes. This paper further proposes a novel method, named Gradient-based CAPS (Grad-CAPS), that modifies CAPS by reducing the difference in the gradient of action and then uses displacement normalization to enable the agent to adapt to invariant action scales. Consequently, our method effectively reduces zigzagging action sequences while enhancing policy expressiveness and the adaptability of our method across diverse scenarios and environments. In the experiments, we integrated Grad-CAPS with different reinforcement learning algorithms and evaluated its performance on various robotic-related tasks in DeepMind Control Suite and OpenAI Gym environments. The results demonstrate that Grad-CAPS effectively improves performance while maintaining a comparable level of smoothness compared to CAPS and Vanilla agents.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.04315
- https://arxiv.org/pdf/2407.04315
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400434631
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https://openalex.org/W4400434631Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.04315Digital Object Identifier
- Title
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Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement LearningWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-05Full publication date if available
- Authors
-
Ickjai Lee, Hoang-Giang Cao, Cong-Tinh Dao, Yu‐Cheng Chen, I‐Chen WuList of authors in order
- Landing page
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https://arxiv.org/abs/2407.04315Publisher landing page
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https://arxiv.org/pdf/2407.04315Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.04315Direct OA link when available
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Reinforcement learning, Regularization (linguistics), Smoothness, Artificial intelligence, Action (physics), Computer science, Reinforcement, Machine learning, Mathematics, Engineering, Mathematical analysis, Physics, Structural engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.zigzagging | 131 |
| abstract_inverted_index.application | 30 |
| abstract_inverted_index.challenges, | 35 |
| abstract_inverted_index.consumption | 53 |
| abstract_inverted_index.demonstrate | 178 |
| abstract_inverted_index.effectively | 129, 181 |
| abstract_inverted_index.intelligent | 20 |
| abstract_inverted_index.maintaining | 185 |
| abstract_inverted_index.performance | 163, 183 |
| abstract_inverted_index.(Grad-CAPS), | 98 |
| abstract_inverted_index.adaptability | 140 |
| abstract_inverted_index.conditioning | 71 |
| abstract_inverted_index.displacement | 114 |
| abstract_inverted_index.experiments, | 151 |
| abstract_inverted_index.trajectories | 43 |
| abstract_inverted_index.Consequently, | 126 |
| abstract_inverted_index.Reinforcement | 1 |
| abstract_inverted_index.applications, | 15 |
| abstract_inverted_index.environments. | 27, 148, 175 |
| abstract_inverted_index.normalization | 115 |
| abstract_inverted_index.reinforcement | 157 |
| abstract_inverted_index.Gradient-based | 96 |
| abstract_inverted_index.expressiveness | 137 |
| abstract_inverted_index.regularization | 81 |
| abstract_inverted_index.robotic-related | 166 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |