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Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Rishav Rishav
, Somjit Nath
, Vincent Michalski
,
Samira Ebrahimi Kahou
University of Calgary
McGill University
University of Montreal
CIFAR AI Chair
Research output
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Contribution to journal
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Journal Article
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peer-review
Overview
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Keyphrases
Behavior Attribution
100%
Behavior Discovery
100%
Explainable Reinforcement Learning
100%
Recurring
50%
Robotics
50%
Healthcare
50%
Reinforcement Learning Agent
50%
Agent Behavior
50%
State Action
50%
Individual Action
50%
Single State
50%
Recurring Patterns
50%
Building Trust
50%
Multiple Trajectories
50%
Action Features
50%
Understanding Why
50%
Cluster Coherence
50%
Multiple Decisions
50%
Behavior Segmentation
50%
High Stakes Applications
50%
Action Sequences
50%
Reinforcement Learning Environment
50%
Offline Reinforcement Learning
50%
Explainability Methods
50%
Human Preferences
50%
Fine-grained Action
50%
Computer Science
Reinforcement Learning
100%
Robotics
33%
Learning Agent
33%
Baseline Level
33%
Recurring Pattern
33%
Action Sequence
33%
Individual Attribute
33%