Model-Based Episodic Memory Induces Dynamic Hybrid ControlsDownload PDF

21 May 2021, 20:43 (modified: 21 Jan 2022, 23:02)NeurIPS 2021 PosterReaders: Everyone
Keywords: Reinforcement learning, episodic memory, sample-efficiency
TL;DR: A model-based episodic control facilitating complementary learning systems
Abstract: Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory. We propose a new model-based episodic memory of trajectories addressing current limitations of episodic control. Our memory estimates trajectory values, guiding the agent towards good policies. Built upon the memory, we construct a complementary learning model via a dynamic hybrid control unifying model-based, episodic and habitual learning into a single architecture. Experiments demonstrate that our model allows significantly faster and better learning than other strong reinforcement learning agents across a variety of environments including stochastic and non-Markovian settings.
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Code: https://github.com/thaihungle/MBEC-plus
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