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Apr 22, 2020 planning abstractions RL
Learning Symbolic Representations for planning

In the pursuit of learning planner from data, I ended up reading Konidaris et al. (JAIR 2018). Getting through this paper was an onerous task. Which I would not like to do again. So, here are my notes on the key concepts from that paper, which are relevant for learning high-level, abstract planner.
Apr 15, 2020 RL GNN neurosymbolic relational
Deep Relational RL

Relational RL has not made a lot of splashes in real-world because it is easier to write a planner than learn a relational RL agent. This might be about to change with the current achievements of the graph based relational reasoning approaches. This article summarizes my understanding of the pioneering work of Vinicius Zambaldi et al. (ICLR 2019) on Deep Relational RL.
Apr 12, 2020 RL relational SRL
Relational Reinforcement Learning

My notes on Džeroski, Sašo, Luc De Raedt, and Kurt Driessens, Machine Learning 2001
Mar 20, 2020 RL
Hindsight Experience Replay

My notes on Marcin Andrychowicz et al. NeurIPS 2017.
Mar 18, 2020 RL imitation generalization
Logical Program Policies

My notes on Tom Silver, Kelsey R. Allen, Alex K. Lew, Leslie Kaelbling, and Josh Tenenbaum, AAAI 2020.
Dec 6, 2019 RL offline summary
Fitted Q and Batch Reinforcement Learning

Some pointers on Batch Reinforcement Learning and Fitted Q. These were gathered while working on an RL for healthcare project, as part of Advanced RL course by Prof. Sriraam Natarajan.
Oct 28, 2019 RL hierarchy coursework
Hierarchical Reinforcement Learning

An overview of Hierarchical RL. Written as part of Advanced RL course by Prof. Sriraam Natarajan.
Oct 5, 2019 RL advice starling
Active Advice Seeking for Inverse Reinforcement Learning

My notes on Phillip Odom and Sriraam Natarajan, AAMAS 2016.
Sep 3, 2019 planning XAI RL
Minimal Sufficient Explanation

My notes on Omar Zia Khan, Pascal Poupart, and James P Black, ICAPS 2009