Here, the RAIL research team focuses on learning abstract representations, which the team believes is an important component if we are ever to apply reinforcement learning to the real world. In particular, the RAIL research team focuses on skill- and symbol discovery, as well as the interplay between the two. The RAIL research team have applied their approaches to challenging pixel-based tasks that require high-level planning and has shown that symbolic representations can be learned directly from low-level sensor data.