Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention

Fleer S, Ritter H (2019)
In: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing , 976. Cham: Springer: 231-240.

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Sammelwerksbeitrag | Veröffentlicht | Englisch
Abstract / Bemerkung
We propose a reinforcement learning approach that combines an asynchronous actor-critic model with a recurrent model of visual attention. Instead of using the full visual information of the scene, the resulting model accumulates the foveal information of controlled glimpses and is thus able to reduce the complexity of the network. Using the designed model, an artificial agent is able to solve a challenging “mediated interaction” task. In these tasks, the desired effects cannot be created through direct interaction, but instead require the learner to discover how to exert suitable effects on the target object through involving a “tool”. To learn the given mediated interaction task, the agent is “actively” searching for salient points within the environment by taking a limited number of fovea-like glimpses. It then uses the accumulated information to decide which action to take next.
Erscheinungsjahr
Buchtitel
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019
Band
976
Seite(n)
231-240
Konferenz
13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)
Konferenzort
Barcelona, Spain
Konferenzdatum
2019-06-26 – 2019-06-28
PUB-ID

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Fleer S, Ritter H. Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention. In: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing . Vol 976. Cham: Springer; 2019: 231-240.
Fleer, S., & Ritter, H. (2019). Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019, Advances in Intelligent Systems and Computing , 976, 231-240. Cham: Springer. doi:10.1007/978-3-030-19642-4_23
Fleer, S., and Ritter, H. (2019). “Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention” in Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019 Advances in Intelligent Systems and Computing , vol. 976, (Cham: Springer), 231-240.
Fleer, S., & Ritter, H., 2019. Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention. In Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing . no.976 Cham: Springer, pp. 231-240.
S. Fleer and H. Ritter, “Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention”, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019, Advances in Intelligent Systems and Computing , vol. 976, Cham: Springer, 2019, pp.231-240.
Fleer, S., Ritter, H.: Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing . 976, p. 231-240. Springer, Cham (2019).
Fleer, Sascha, and Ritter, Helge. “Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention”. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Cham: Springer, 2019.Vol. 976. Advances in Intelligent Systems and Computing . 231-240.
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