Related papers: Adaptive Rational Activations to Boost Deep Reinforcement Learning

Adaptive Rational Activations to Boost Deep Reinforcement Learning

URL: http://arxiv.org/abs/2102.09407v5
Date: Sat, 16 Mar 2024 12:40:45 GMT
Title: Adaptive Rational Activations to Boost Deep Reinforcement Learning
Authors: Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting,
Abstract summary: We motivate why rationals are suitable for adaptable activation functions and why their inclusion into neural networks is crucial. We demonstrate that equipping popular algorithms with (recurrent-)rational activations leads to consistent improvements on Atari games.
Score: 68.10769262901003
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated. This perspective should be critical in the context of constantly changing distinct reinforcement learning environments, yet current approaches still primarily employ static activation functions. In this work, we motivate why rationals are suitable for adaptable activation functions and why their inclusion into neural networks is crucial. Inspired by recurrence in residual networks, we derive a condition under which rational units are closed under residual connections and formulate a naturally regularised version: the recurrent-rational. We demonstrate that equipping popular algorithms with (recurrent-)rational activations leads to consistent improvements on Atari games, especially turning simple DQN into a solid approach, competitive to DDQN and Rainbow.

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