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Reinforcement learning is an appropriate and successful method to robustlyperform low-level robot control under noisy conditions. Symbolic actionplanning is useful to resolve causal dependencies and to break a causallycomplex problem down into a sequence of simpler high-level actions. A problemwith the integration of both approaches is that action planning is based ondiscrete high-level action- and state spaces, whereas reinforcement learning isusually driven by a continuous reward function. However, recent advances inreinforcement learning, specifically, universal value function approximatorsand hindsight experience replay, have focused on goal-independent methods basedon sparse rewards. In this article, we build on these novel methods tofacilitate the integration of action planning with reinforcement learning byexploiting the reward-sparsity as a bridge between the high-level and low-levelstate- and control spaces. As a result, we demonstrate that the integratedneuro-symbolic method is able to solve object manipulation problems thatinvolve tool use and non-trivial causal dependencies under noisy conditions,exploiting both data and knowledge.

From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving