On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning

11 April 2024

Papers citing "On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning"

9 / 9 papers shown

Title
Deep Laplacian-based Options for Temporally-Extended Exploration Martin Klissarov Marlos C. Machado OffRL 64 20 0 26 Jan 2023
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity Abhishek Gupta Aldo Pacchiano Yuexiang Zhai Sham Kakade Sergey Levine OffRL 68 67 0 18 Oct 2022
Does Zero-Shot Reinforcement Learning Exist? Ahmed Touati Jérémy Rapin Yann Ollivier OffRL 73 43 0 29 Sep 2022
Heuristic-Guided Reinforcement Learning Ching-An Cheng Andrey Kolobov Adith Swaminathan OffRL 59 62 0 05 Jun 2021
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping Yujing Hu Weixun Wang Hangtian Jia Yixiang Wang Yingfeng Chen Jianye Hao Feng Wu Changjie Fan OffRL 57 176 0 05 Nov 2020
Dota 2 with Large Scale Deep Reinforcement Learning OpenAI OpenAI : Christopher Berner Greg Brockman Brooke Chan ... Szymon Sidor Ilya Sutskever Jie Tang Filip Wolski Susan Zhang GNN VLM CLL AI4CE LRM 140 1,819 0 13 Dec 2019
Unsupervised State Representation Learning in Atari Ankesh Anand Evan Racah Sherjil Ozair Yoshua Bengio Marc-Alexandre Côté R. Devon Hjelm SSL 51 255 0 19 Jun 2019
The Arcade Learning Environment: An Evaluation Platform for General Agents Marc G. Bellemare Yavar Naddaf J. Veness Michael Bowling 100 3,002 0 19 Jul 2012
Potential-Based Shaping and Q-Value Initialization are Equivalent Eric Wiewiora OffRL 58 178 0 26 Jun 2011