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Successor Feature Neural Episodic Control
David Emukpere · Xavier Alameda-Pineda · Chris Reinke
Event URL: https://openreview.net/forum?id=e1Q2_jaE08J »

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.

Author Information

David Emukpere (Inria)
Xavier Alameda-Pineda (INRIA)
Chris Reinke (INRIA)

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