Model-Free Episodic Control with State Aggregation

Abstract

Episodic control provides a highly sample-efficient method for reinforcement learning while enforcing high memory and computational requirements. This work proposes a simple heuristic for reducing these requirements, and an application to Model-Free Episodic Control (MFEC) is presented. Experiments on Atari games show that this heuristic successfully reduces MFEC computational demands while producing no significant loss of performance when conservative choices of hyperparameters are used. Consequently, episodic control becomes a more feasible option when dealing with reinforcement learning tasks.

8 pages, 21 figures

References in corpus (3)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Rainbow: Combining Improvements in Deep Reinforcement Learning
• Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means