December 18, 2017 preprint Open access Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents arXiv (Cornell University) DOI: 10.48550/arxiv.1712.06560 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 1 stored of 110 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Multi-Agent Reinforcement Learning 2022 Springer eBooks book-chapter Computer Science Reinforcement Learning in Robotics 4 citations 1 result References 0