Proximal Distilled Evolutionary Reinforcement Learning AAAI2020. paper
Cristian Bodnar, Ben Day, Pietro Lio ́
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents AAAI2020. paper
Felipe Leno da Silva (University of Sao Paulo); Pablo Hernandez-Leal (Borealis AI); Bilal Kartal (Borealis AI); Matthew Taylor (Borealis AI)*
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning AAAI2020. paper
Nicolas Anastassacos, Stephen Hailes, Mirco Musolesi
Reinforcement Learning with Perturbed Reward AAAI2020. paper
Jingkang Wang, Yang Liu, Bo Li
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization AAAI2020. paper
Qi Zhou, HouQiang Li, Jie Wang
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes AAAI2020. paper
Toma ́sˇ Bra ́zdil, Krishnendu Chatterjee, Petr Novotny ́, Jirˇ ́ı Vahala
Exploratory Combinatorial Optimization with Reinforcement Learning AAAI2020. paper
Thomas D. Barrett, William R. Clements, Jakob N. Foerster, Alex I. Lvovsky
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning AAAI2020. paper
Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》