  Multi-Task Reinforcement Learning aims at developing agents that are able to
continually evolve and adapt to new scenarios. However, this goal is
challenging to achieve due to the phenomenon of catastrophic forgetting and the
high demand of computational resources. Learning from scratch for each new task
is not a viable or sustainable option, and thus agents should be able to
collect and exploit prior knowledge while facing new problems. While several
methodologies have attempted to address the problem from different
perspectives, they lack a common structure. In this work, we propose a new
framework, I Know How (IKH), which provides a common formalization. Our
methodology focuses on modularity and compositionality of knowledge in order to
achieve and enhance agent's ability to learn and adapt efficiently to dynamic
environments. To support our framework definition, we present a simple
application of it in a simulated driving environment and compare its
performance with that of state-of-the-art approaches.
