163 citations · 589 across the 42 of their papers we have counts for
11 papers · 1 filter
Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL
Kin-Ho Lam, Delyar Tabatabai, Jed Irvine +4
Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited e…
Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations
Kin-Ho Lam, Zhengxian Lin, Jed Irvine +5
Enabling humans to identify potential flaws in an agent's decision making is an important Explainable AI application. We consider identifying such flaws in a planning-based deep re…
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions
Zhengxian Lin, Kim-Ho Lam, Alan Fern
We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values…
The Choice Function Framework for Online Policy Improvement
Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli
There are notable examples of online search improving over hand-coded or learned policies (e.g. AlphaZero) for sequential decision making. It is not clear, however, whether or not…
Visualizing and Understanding Atari Agents
Sam Greydanus, Anurag Koul, Jonathan Dodge +1
While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward…
A Meta-Analysis of the Anomaly Detection Problem
Andrew Emmott, Shubhomoy Das, Thomas Dietterich +2
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across…