One of the challenges for artificial agents is managing the complexity of their environment as they learn tasks especially if they are grounded in the physical world. A scalable solution to address the state explosion problem is thus a prerequisite of physically grounded, agentbased systems. This paper presents a framework for developing grounded, symbolic representations aimed at scaling subsequent learning as well as forming a basis for symbolic reasoning. These symbols partition the environment so the agent need only consider an abstract view of the original space when learning new tasks and allows it to apply acquired symbols to novel situations.
['Marcus Carlos Oladell', 'Manfred Huber']
Symbol generation and grounding for reinforcement learning agents using affordances and dictionary compression