134 research outputs found
Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has been successfully applied in several
research domains such as robot navigation and automated video game playing.
However, these methods require excessive computation and interaction with the
environment, so enhancements on sample efficiency are required. The main reason
for this requirement is that sparse and delayed rewards do not provide an
effective supervision for representation learning of deep neural networks. In
this study, Proximal Policy Optimization (PPO) algorithm is augmented with
Generative Adversarial Networks (GANs) to increase the sample efficiency by
enforcing the network to learn efficient representations without depending on
sparse and delayed rewards as supervision. The results show that an increased
performance can be obtained by jointly training a DRL agent with a GAN
discriminator.
----
Derin Pekistirmeli Ogrenme, robot navigasyonu ve otomatiklestirilmis video
oyunu oynama gibi arastirma alanlarinda basariyla uygulanmaktadir. Ancak,
kullanilan yontemler ortam ile fazla miktarda etkilesim ve hesaplama
gerektirmekte ve bu nedenle de ornek verimliligi yonunden iyilestirmelere
ihtiyac duyulmaktadir. Bu gereksinimin en onemli nedeni, gecikmeli ve seyrek
odul sinyallerinin derin yapay sinir aglarinin etkili betimlemeler
ogrenebilmesi icin yeterli bir denetim saglayamamasidir. Bu calismada,
Proksimal Politika Optimizasyonu algoritmasi Uretici Cekismeli Aglar (UCA) ile
desteklenerek derin yapay sinir aglarinin seyrek ve gecikmeli odul sinyallerine
bagimli olmaksizin etkili betimlemeler ogrenmesi tesvik edilmektedir. Elde
edilen sonuclar onerilen algoritmanin ornek verimliliginde artis elde ettigini
gostermektedir.Comment: in Turkis
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
In recent years, the development of robotics and artificial intelligence (AI)
systems has been nothing short of remarkable. As these systems continue to
evolve, they are being utilized in increasingly complex and unstructured
environments, such as autonomous driving, aerial robotics, and natural language
processing. As a consequence, programming their behaviors manually or defining
their behavior through reward functions (as done in reinforcement learning
(RL)) has become exceedingly difficult. This is because such environments
require a high degree of flexibility and adaptability, making it challenging to
specify an optimal set of rules or reward signals that can account for all
possible situations. In such environments, learning from an expert's behavior
through imitation is often more appealing. This is where imitation learning
(IL) comes into play - a process where desired behavior is learned by imitating
an expert's behavior, which is provided through demonstrations.
This paper aims to provide an introduction to IL and an overview of its
underlying assumptions and approaches. It also offers a detailed description of
recent advances and emerging areas of research in the field. Additionally, the
paper discusses how researchers have addressed common challenges associated
with IL and provides potential directions for future research. Overall, the
goal of the paper is to provide a comprehensive guide to the growing field of
IL in robotics and AI.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
- …