Discovering faster matrix multiplication algorithms with reinforcement learning
Reinforcement Learning under Partial Observability Guided by Learned Environment Models
In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environment. Therefore, we propose an approach for reinforcement learning (RL) in partially observable environments. While assuming that the environment behaves like a partially observable Markov decision process with known discrete actions, we assume no knowledge about its structure or transition probabilities.
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Reinforcement Learning - Introduction
Causal Reinforcement Learning -- Part 2/2 (ICML tutorial)
Causal Reinforcement Learning -- Part 1/2 (ICML tutorial)
Reinforcement Learning for Precision Oncology
The accelerating merger of information technology and cancer research heralds the advent of novel methods and models for clinical decision making in oncology. Reinforcement learning—as one of the major subspecialties in machine learning—holds the potential for the development of high-performance decision support tools. However, many recent studies of reinforcement learning in oncology suffer from common shortcomings and pitfalls that need to be addressed for the development of safe, interpretable and reliable algorithms for future clinical practice.
Chip Placement with Deep Reinforcement Learning
Algorithms for Inverse Reinforcement Learning
DSA ADS Course - 2021
ICML '00: Proceedings of the Seventeenth International Conference on Machine LearningJune 2000 Pages 663–670