We propose Ephemeral Value Adjusments (EVA): a means of allowing deep
reinforcement learning agents to rapidly adapt to experience in their replay
buffer. EVA shifts the value predicted by a neural network with an estimate of
the value function found by planning over experience tuples from the replay
buffer near the current state. EVA combines a number of recent ideas around
combining episodic memory-like structures into reinforcement learning agents:
slot-based storage, content-based retrieval, and memory-based planning. We show
that EVAis performant on a demonstration task and Atari games.Comment: Accepted at NIPS 201

Hansen, Steven

Sprechmann, Pablo

Pritzel, Alexander

Barreto, André

Blundell, Charles

English

arXiv

[Excerpt] The affect theory of social exchange places emotion and feelings at the center of social exchange theorizing (Lawler 2001). It posits that exchange generates emotions and that emotions are internal responses that reward and punish actors. Emotions that occur regularly in exchange processes include feeling good about successful exchange, feeling shame about the terms accepted, feeling gratitude toward a conciliatory exchange partner, and feeling anger at a difficult or hostile exchange partner. The theory argues that such emotions and feelings have important consequences for the relations, networks, and groups within which they occur.Lawler78_The_affect_theory_of_social_exchange.pdf: 137 downloads, before Oct. 1, 2020

Lawler, Edward J.

eCommons@Cornell

The Affect Theory of Social Exchange

[Excerpt] The affect theory of social exchange places emotion and feelings at the center of social exchange theorizing (Lawler 2001). It posits that exchange generates emotions and that emotions are internal responses that reward and punish actors. Emotions that occur regularly in exchange processes include feeling good about successful exchange, feeling shame about the terms accepted, feeling gratitude toward a conciliatory exchange partner, and feeling anger at a difficult or hostile exchange partner. The theory argues that such emotions and feelings have important consequences for the relations, networks, and groups within which they occur

Lawler, Edward J

DigitalCommons@ILR

https://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article=2272&amp;context=articles

Fast deep reinforcement learning using online adjustments from the past

Abstract

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eCommons@Cornell

DigitalCommons@ILR