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Self-Improving Generative Adversarial Reinforcement Learnin:

, In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems,

Liu, Yang ; Zeng, Yifeng ; Chen, Yingke.. - p. 52-60 , 2019

Link: https://dl.acm.org/doi/10.5555/3306127.3331673

RT 
T1 <cTitle>Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems</cTitle>: T1 <Title>Self-Improving Generative Adversarial Reinforcement Learning</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3331673&Exemplar=1&LAN=DE
				
A1 Liu, Yang
A1 Zeng, Yifeng
A1 Chen, Yingke
A1 Tang, Jing
A1 Pan, Yinghui
PB International Foundation for Autonomous Agents and Multiagent Systems
YR 2019
K1 deep learning
K1 generative adversarial nets
K1 imitation learning
K1 policy distillation
K1 policy iteration
K1 reinforcement learning
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Reinforcement learning
K1 Apprenticeship learning
K1 Computing methodologies
K1 Machine learning
K1 Machine learning algorithms
K1 Dynamic programming for Markov decision processes
K1 Policy iteration
K1 Learning paradigms
K1 Adversarial learning
K1 Sequential decision making
K1 Algorithmic game theory and mechanism design
K1 Convergence and learning in games
K1 Machine learning approaches
K1 Neural networks
K1 Inverse reinforcement learning
SP 52
OP 60
LK http://dx.doi.org/https://dl.acm.org/doi/10.5555/3306127.3331673
DO https://dl.acm.org/doi/10.5555/3306127.3331673
SF ELIB - SuUB Bremen

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