Keywords: Planning, Abstraction, Hierarchical RL, DQN
TL;DR: Combining a symbolic planner and a Deep RL agent can help achieve the generalization ability of the planner with the effective learning ability of Deep RL
Abstract: We consider the problem of combining symbolic planning and deep reinforcement learning (RL) to achieve the best of both worlds -- the generalization ability of the planner with the effective learning ability of deep RL. To this effect, we extend a previous work of Kokel et al. 2021, RePReL, to deep RL. As we demonstrate in experiments in two relational worlds, this combined framework enables effective learning, transfer and generalization when compared to the use of an end-to-end deep RL framework.
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