Domain-Adversarial and -Conditional State Space Model for Imitation Learning
Ryo Okumura,Masashi Okada,Tadahiro Taniguchi,Ryo Okumura,Masashi Okada,Tadahiro Taniguchi
State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, acquiring domain-agnostic states is essential for achieving efficient imitation learning. Without these states, imitation learning is hampered by domain-dependent information useless for control. However, existing methods...
