Model-Free Episodic Control with State Aggregation

2020-08-21 · Rafael Pinto

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.

📄 PDF Abstract BibTeX arXiv:2008.09685

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Tasks

Atari Gamesmodelreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

MFEC Non-parametric approximation of Q-values by storing all visited states and doing inference through k-Nearest Neighbors.

Solving Continuous Control with Episodic Memory

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Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms show benefits of using episodic-based data …

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2022-11-28 · Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat

Non-parametric episodic memory can be used to quickly latch onto high-rewarded experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning approaches in which reward signals need to …

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Is prioritized sweeping the better episodic control?

2017-11-20 · Johanni Brea

Episodic control has been proposed as a third approach to reinforcement learning, besides model-free and model-based control, by analogy with the three types of human memory. i.e. episodic, procedural and semantic memory…

Reinforcement Learning