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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. We present Hierarchical -DQN (h-DQN), a framework to integrate Hierarchical action-value functions, op-erating at different temporal scales, with goal-driven intrinsically motivated deepreinforcement learning.
levels of temporal abstraction is a key challenge in tasks involving long-range planning. In the context of hierarchical reinforcement learning [2], Sutton et al.[34] proposed the options framework, which involves abstractions over the space of actions. At each step, the agent chooses either a one-
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