Planning in the imagination: High-level planning on learned abstract search spaces
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We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space of its own creation that is completely decoupled from the real environment. Unlike prior approaches, this enables the agent to perform high-level planning at arbitrary timescales and reason in terms of compound or temporally-extended actions, which can be useful in environments where large numbers of base-level micro-actions are needed to perform relevant macro-actions. In addition, our method is more general than comparable prior methods because it handles settings with continuous action spaces and partial observability. We evaluate our method on multiple domains, including navigation tasks and Sokoban. Experimentally, it outperforms comparable prior methods without assuming access to an environment simulator.
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