  A longstanding goal in reinforcement learning is to build intelligent agents
that show fast learning and a flexible transfer of skills akin to humans and
animals. This paper investigates the integration of two frameworks for tackling
those goals: episodic control and successor features. Episodic control is a
cognitively inspired approach relying on episodic memory, an instance-based
memory model of an agent's experiences. Meanwhile, successor features and
generalized policy improvement (SF&GPI) is a meta and transfer learning
framework allowing to learn policies for tasks that can be efficiently reused
for later tasks which have a different reward function. Individually, these two
techniques have shown impressive results in vastly improving sample efficiency
and the elegant reuse of previously learned policies. Thus, we outline a
combination of both approaches in a single reinforcement learning framework and
empirically illustrate its benefits.
