Value function estimation using conditional diffusion models for control Article Swipe
Bogdan Mazoure
,
Walter Talbott
,
Miguel Ángel Bautista
,
Devon Hjelm
,
Alexander Toshev
,
Josh Susskind
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07290
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07290
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative to address, sooner than later, the potential problem of running out of high-quality demonstrations. In this case, instead of collecting only new data via costly human demonstrations or risking a simulation-to-real transfer with uncertain effects, it would be beneficial to leverage vast amounts of readily-available low-quality data. Since classical control algorithms such as behavior cloning or temporal difference learning cannot be used on reward-free or action-free data out-of-the-box, this solution warrants novel training paradigms for continuous control. We propose a simple algorithm called Diffused Value Function (DVF), which learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. This model can be efficiently learned from state sequences (i.e., without access to reward functions nor actions), and subsequently used to estimate the value of each action out-of-the-box. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers, and show promising qualitative and quantitative results on challenging robotics benchmarks.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.07290
- https://arxiv.org/pdf/2306.07290
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380687059
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https://openalex.org/W4380687059Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.07290Digital Object Identifier
- Title
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Value function estimation using conditional diffusion models for controlWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-09Full publication date if available
- Authors
-
Bogdan Mazoure, Walter Talbott, Miguel Ángel Bautista, Devon Hjelm, Alexander Toshev, Josh SusskindList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.07290Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2306.07290Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2306.07290Direct OA link when available
- Concepts
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Computer science, Leverage (statistics), Artificial intelligence, Reinforcement learning, Machine learning, Bellman equation, Function (biology), Action (physics), Robotics, Stability (learning theory), Robot, Mathematical optimization, Mathematics, Evolutionary biology, Physics, Biology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.behavior | 90 |
| abstract_inverted_index.control. | 113 |
| abstract_inverted_index.dynamics | 134 |
| abstract_inverted_index.effects, | 71 |
| abstract_inverted_index.estimate | 160 |
| abstract_inverted_index.learning | 7, 95 |
| abstract_inverted_index.leverage | 77 |
| abstract_inverted_index.multiple | 182 |
| abstract_inverted_index.provided | 18 |
| abstract_inverted_index.reliable | 2 |
| abstract_inverted_index.robotics | 193 |
| abstract_inverted_index.solution | 106 |
| abstract_inverted_index.temporal | 93 |
| abstract_inverted_index.training | 25, 109 |
| abstract_inverted_index.transfer | 68 |
| abstract_inverted_index.warrants | 107 |
| abstract_inverted_index.actions), | 155 |
| abstract_inverted_index.algorithm | 118 |
| abstract_inverted_index.classical | 85 |
| abstract_inverted_index.diffusion | 137 |
| abstract_inverted_index.functions | 153 |
| abstract_inverted_index.paradigms | 110 |
| abstract_inverted_index.potential | 43 |
| abstract_inverted_index.promising | 186 |
| abstract_inverted_index.sequences | 147 |
| abstract_inverted_index.uncertain | 70 |
| abstract_inverted_index.algorithms | 87 |
| abstract_inverted_index.beneficial | 75 |
| abstract_inverted_index.collecting | 56 |
| abstract_inverted_index.continuous | 112 |
| abstract_inverted_index.difference | 94 |
| abstract_inverted_index.imperative | 36 |
| abstract_inverted_index.increases, | 33 |
| abstract_inverted_index.multi-step | 128 |
| abstract_inverted_index.visitation | 179 |
| abstract_inverted_index.action-free | 102 |
| abstract_inverted_index.benchmarks. | 194 |
| abstract_inverted_index.challenging | 192 |
| abstract_inverted_index.efficiently | 143, 175 |
| abstract_inverted_index.interaction | 133 |
| abstract_inverted_index.low-quality | 82 |
| abstract_inverted_index.parameters, | 17 |
| abstract_inverted_index.performance | 11 |
| abstract_inverted_index.qualitative | 187 |
| abstract_inverted_index.reward-free | 100 |
| abstract_inverted_index.controllers, | 183 |
| abstract_inverted_index.high-quality | 49 |
| abstract_inverted_index.quantitative | 189 |
| abstract_inverted_index.subsequently | 157 |
| abstract_inverted_index.complimentary | 20 |
| abstract_inverted_index.reinforcement | 6 |
| abstract_inverted_index.demonstrations | 63 |
| abstract_inverted_index.demonstrations. | 50 |
| abstract_inverted_index.out-of-the-box, | 104 |
| abstract_inverted_index.out-of-the-box. | 166 |
| abstract_inverted_index.environment-robot | 132 |
| abstract_inverted_index.readily-available | 81 |
| abstract_inverted_index.simulation-to-real | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |