| Presentation | 2005/3/7 Stochastic Policy Representation Using a Multidimensional Normal Distribution for Actor-critic Methods Satoshi ABE, Atsushi UENO, Masatsugu KIDODE, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Actor-critic methods, which is one of reinforcement learning methods, is applied to that problems easily, and has left many achievements. Generaly, normal distribution has been used as probability distribution on which agent selects action. Agent renews means and standard deviation through policy parameter for selecting appropriate action intercting with environment. Under assumption that output dimensions are individual, conventional methods use normal distribution. Problems, such as trajectory planning of manupulator, and robot walking control etc., every output must cooperate with each other. Conventional methods cannot make consideration correlation, so it takes long time to get policy selecting action cooperately and being high performance. In this paper, we aim that learning speed up and improvement performance by adopting multivariate normal distribution with variance and covariance matrix into probability distribution selecting action. we have some experiments to demonstrate availability of this method by trajectory planning of manipulator. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | reinforcement learning / actor-critic methods / multidimensional normal distribution / manipulator |
| Paper # | AI2004-72 |
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| Conference Information | |
| Committee | AI |
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| Conference Date | 2005/3/7(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | |
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| Paper Information | |
| Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Stochastic Policy Representation Using a Multidimensional Normal Distribution for Actor-critic Methods |
| Sub Title (in English) | |
| Keyword(1) | reinforcement learning |
| Keyword(2) | actor-critic methods |
| Keyword(3) | multidimensional normal distribution |
| Keyword(4) | manipulator |
| 1st Author's Name | Satoshi ABE |
| 1st Author's Affiliation | Nara Institute of Science and Technology() |
| 2nd Author's Name | Atsushi UENO |
| 2nd Author's Affiliation | Osaka City University |
| 3rd Author's Name | Masatsugu KIDODE |
| 3rd Author's Affiliation | Nara Institute of Science and Technology |
| Date | 2005/3/7 |
| Paper # | AI2004-72 |
| Volume (vol) | vol.104 |
| Number (no) | 726 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |