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Episodic memory plays an important role in the behavior of animals andhumans. It allows the accumulation of information about current state of theenvironment in a task-agnostic way. This episodic representation can be lateraccessed by down-stream tasks in order to make their execution more efficient.In this work, we introduce the neural architecture with shared episodic memory(SEM) for learning and the sequential execution of multiple tasks. Weexplicitly split the encoding of episodic memory and task-specific memory intoseparate recurrent sub-networks. An agent augmented with SEM was able toeffectively reuse episodic knowledge collected during other tasks to improveits policy on a current task in the Taxi problem. Repeated use of episodicrepresentation in continual learning experiments facilitated acquisition ofnovel skills in the same environment.

Continual and Multi-task Reinforcement Learning With Shared Episodic Memory