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Publication

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Volkan Cevher, Kamalaruban Parameswaran
2020
Journal Articles

Abstract

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, smart-home automation, and service robots, among many others. Despite these remarkable achievements, many basic tasks can still elude a single RL agent. Examples abound, from multiplayer games, multirobots, cellular-antenna tilt control, traffic-control systems, and smart power grids to network management.

Official source

https://infoscience.epfl.ch/entities/publication/4e0aacc8-aa73-4d18-8550-9a2d242f8bb7

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Volkan Cevher, Kamalaruban Parameswaran
2020
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4e0aacc8-aa73-4d18-8550-9a2d242f8bb7

About this result

Related concepts (27)

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Deep reinforcement learning

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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