Transcription of Hierarchical Deep Reinforcement Learning: Integrating ...
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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction andIntrinsic MotivationTejas D. Kulkarni DeepMind, R. Narasimhan CSAIL, SaeediCSAIL, B. TenenbaumBCS, goal-directed behavior in environments with sparse feedback is a majorchallenge for Reinforcement learning algorithms. One of the key difficulties is in-sufficient exploration, resulting in an agent being unable to learn robust motivated agents can explore new behavior for their own sake ratherthan to directly solve external goals. Such intrinsic behaviors could eventuallyhelp the agent solve tasks posed by the environment.
Other related work for hierarchical formulations include the MAXQ framework [6], which decom-posed the value function of an MDP into combinations of value functions of smaller constituent ... Baranes et al.[1] have proposed a goal-driven active learning approach for learning skills in continuous sensorimotor spaces.
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