1,824,425 research outputs found
Replication Data for: Zhirnov, Andrei. 2019. “Decision Period and Duverger’s Psychological Effect: Evidence from India.” Electoral Studies 58: 21-30.
No full textThe datasets and code used in the main analyses presented in Zhirnov, Andrei. 2019. “Decision Period and Duverger’s Psychological Effect: Evidence
from India.” Electoral Studies 58: 21-30
Replication Data for: Zhirnov, Andrei. 2019. “Decision Period and Duverger’s Psychological Effect: Evidence from India.” Electoral Studies 58: 21-30.
No full textThe datasets and code used in the main analyses presented in Zhirnov, Andrei. 2019. “Decision Period and Duverger’s Psychological Effect: Evidence
from India.” Electoral Studies 58: 21-30
Artista invitado Harvy Andrei Oviedo Vásquez
No full textExhibition of works by the artist Harvy Andrei Oviedo VásquezExposición de obras del artista Harvy Andrei Oviedo Vásquez Exposición de obras del artista Harvy Andrei Oviedo Vásquez 
Andrei Minchenko Interview undated
No full textNOTE: to view these items please visit http://dynkincollection.library.cornell.eduUndated interview conducted by Eugene Dynkin with Andrei N.Minchenko
Andrei Toom Interview
No full textNOTE: to view these items please visit http://dynkincollection.library.cornell.eduInterview conducted by Eugene Dynkin with Andrei Toom on February 23, 1990 in Ithaca, New York
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Andrei Ochir-Goryaev, Sanan Kogaev, Jangar
No full textAndrei and his grandson Sanan recite from the epos Jangar
Andrei Gabrielov interview May 3, 1999
No full textNOTE: to view these items please visit http://dynkincollection.library.cornell.eduInterview conducted by Eugene Dynkin with Andrei Gabrielov on May 3, 1999
Multi-Agent Reinforcement Learning using Centralized Critics in Collaborative Environments
No full textAgents trained through single-agent reinforcement learning methods such as self-play can provide a good level of performance in multi-agent settings and even in fully cooperative environments. However, most of the time, training multiple agents together using single-agent self-play yields poor results as each agent tries to learn how to perform their task while their teammates are also learning. Thus, training models to reach an optimal behaviour in such situations becomes a challenging, if not impossible issue to overcome. One possible solution to deal with this problem is to facilitate a centralized training process in which the policies of all agents are evaluated by a centralized critic that has access to the observations and actions of all the agents in the environment. By using this approach, the environment becomes stationary and the agents learn in a similar way to using a single-agent algorithm in settings where only one agent needs to be trained. In this paper, we test whether by using a multi-agent reinforcement learning algorithm with centralized critics, as opposed to single-agent ones, we would obtain an agent that generalizes better to new partners in a collaborative environment such as Overcooked, where coordination is critical for good performance. The results display a similar performance between the two algorithms when evaluated through self-play and slightly better or worse results when paired with the human model, representing a mediocre agent, depending on the map. Thus, the multi-agent, centralized critics algorithm used in this study did not train agents that generalize better to new partners. However, the training metrics clearly indicate that the centralized critics method makes the agents learn and converge twice as fast as its single-agent version.https://github.com/andrei-07/rp-overcooked-centralized-critics Link to GitHub repositoryCSE3000 Research ProjectComputer Science and Engineerin
ATLAS Cavern by Andrei Duman, Phase One ambassador
No full textATLAS Cavern by Andrei Duman, Phase One ambassado
Last of the Kodak”: Andrei Tarkovsky’s Struggle With Colour’
Get PDFIn interviews and writings throughout his career, Andrei Tarkovsky repeatedly returned to the theme of cinematic colour. He referred to it in order to repudiate it: colour film was ‘monstrous’ and ‘false’, an artistic ‘blind alley’. Despite his objections, Tarkovsky also repeatedly struggled with the Soviet bureaucracy to secure the use of Eastman Kodak colour negatives. Having secured the use of colour, he then minimised its impact in his films through a combination of desaturated production design and laboratory techniques, and counterbalanced its presence with repeated transitions between colour and black-and-white sequences. This essay explores the contradictions in Tarkovsky’s response to colour. It roots his work in the stagnation-era political economy of the Soviet Union, before moving on to an exploration of the ways in which his chromatic ambivalence manifested itself in the aesthetics of his films. The essay concludes by suggesting a final contradiction, namely that Tarkovsky’s chromatic conservatism anticipated the colour aesthetics of digital cinema
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