Inter-module credit assignment in modular reinforcement learning

By Kazuyuki Samejima, Kenji Doya, Mitsuo Kawato. 2003

65 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “Numerous alternative HRL techniques have been proposed (e.g., Ring, 1991, 1994; Jameson, 1991; Tenenberg et al., 1993; Moore and Atkeson, 1995; Precup et al., 1998; Dietterich, 2000b; Menache et al., 2002; Doya et al., 2002; Ghavamzadeh and Mahadevan, 2003; Barto and Mahadevan, 2003; Samejima et al.”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “Numerous alternative HRL techniques have been proposed [204, 206, 117, 279, 295, 171, 195, 50, 162, 51, 15, 215, 11, 260].”
Learning to Compose Skills (2017, methodology): “In this sense, modular reinforcement learning is a closer analogue to our approach [19, 23, 5, 1].”
Embodied Symbol Emergence Based on Mimesis Theory (2004, methodology): “In an imitation learning framework MOSAIC, which has b en proposed by Samejima et al. (2002) and Samejima, Doya, and Kawato (2003), plural dynamics and inverse dynamics modules for the prediction and control of motion are implemented in order to imitate the motion of others.”
Computational models of reinforcement learning: the role of dopamine as a reward signal (2010, background): “3; Bertin et al. 2007; and for other application of the MMRL framework Doya et al. 2002; Samejima et al. 2003 ).”
Exploration from Generalization Mediated by Multiple Controllers (2013, background): “Hierarchical RL as a whole is described, for instance, in Botvinick et al. (2009), Samejima et al. (2003), and Sutton et al. (1999).”