Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information

12 March 2021

Papers citing "Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information"

8 / 8 papers shown

Title
DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition Yuki Kadokawa Jonas Frey Takahiro Miki Takamitsu Matsubara Marco Hutter 36 0 0 09 May 2025
Iteratively Learn Diverse Strategies with State Distance Information Wei Fu Weihua Du Jingwei Li Sunli Chen Jingzhao Zhang Yi Wu 51 3 0 23 Oct 2023
Diverse Policies Converge in Reward-free Markov Decision Processe Fanqing Lin Shiyu Huang Weiwei Tu 30 0 0 23 Aug 2023
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning T. Kanazawa Chetan Gupta 29 0 0 15 Mar 2023
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery Félix Chalumeau Raphael Boige Bryan Lim Valentin Macé Maxime Allard Arthur Flajolet Antoine Cully Thomas Pierrot 26 21 0 06 Oct 2022
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation Kang Xu Yan Ma Bingsheng Wei Wei Li 32 3 0 24 Sep 2022
Multimodal Trajectory Optimization for Motion Planning Takayuki Osa 34 55 0 16 Mar 2020
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills Xue Bin Peng Pieter Abbeel Sergey Levine M. van de Panne AI4CE 175 494 0 08 Apr 2018